byt5

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer

Bases: PreTrainedTokenizer

Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding.

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

PARAMETER	DESCRIPTION
eos_token	The end of sequence token. 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. TYPE: `str`, *optional*, defaults to `"</s>"` DEFAULT: '</s>'
unk_token	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. TYPE: `str`, *optional*, defaults to `"<unk>"` DEFAULT: '<unk>'
pad_token	The token used for padding, for example when batching sequences of different lengths. TYPE: `str`, *optional*, defaults to `"<pad>"` DEFAULT: '<pad>'
extra_ids	Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are accessible as "" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are indexed from the end of the vocabulary up to beginning ("" is the last token in the vocabulary like in ByT5 preprocessing see here). TYPE: `int`, *optional*, defaults to 125 DEFAULT: 125
additional_special_tokens	Additional special tokens used by the tokenizer. TYPE: `List[str]`, *optional* DEFAULT: None

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

class ByT5Tokenizer(PreTrainedTokenizer):
    """
    Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding.

    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:
        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>

        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.
        extra_ids (`int`, *optional*, defaults to 125):
            Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
            accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
            indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
            like in ByT5 preprocessing see
            [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
        additional_special_tokens (`List[str]`, *optional*):
            Additional special tokens used by the tokenizer.
    """
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        extra_ids=125,
        additional_special_tokens=None,
        **kwargs,
    ) -> None:
        """
        This method initializes an instance of the ByT5Tokenizer class.

        Args:
            self: The instance of the ByT5Tokenizer class.
            eos_token (str, optional): The end-of-sequence token. Default is '</s>'.
            unk_token (str, optional): The unknown token. Default is '<unk>'.
            pad_token (str, optional): The padding token. Default is '<pad>'.
            extra_ids (int, optional): The number of extra special tokens. Default is 125.
            additional_special_tokens (list, optional): List of additional special tokens. Default is None.

        Returns:
            None.

        Raises:
            ValueError: Raised if both extra_ids and additional_special_tokens are provided and
                the additional_special_tokens do not include all extra_ids tokens.
        """
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
            additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
        elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0:
            # Check that we have the right number of extra_id special tokens
            extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
            if extra_tokens != extra_ids:
                raise ValueError(
                    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
                    " extra_ids tokens"
                )

        pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token
        # we force left and right stripping for backward compatibility. The byt5tests depend on this.
        eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token
        # unk token needs to be in the vocab with correct index
        self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token}
        self.offset = len(self._added_tokens_decoder)
        self._utf_vocab_size = 2**8  # utf is 8 bits
        super().__init__(
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            extra_ids=0,
            additional_special_tokens=additional_special_tokens,  # TODO extra ids are not used :sweatywmile:
            **kwargs,
        )

    @property
    def vocab_size(self):
        """
        Method to retrieve the vocabulary size of the ByT5Tokenizer instance.

        Args:
            self: ByT5Tokenizer instance. The self parameter refers to the instance of the ByT5Tokenizer class.

        Returns:
            int: The vocabulary size of the tokenizer. This value represents the total number of unique tokens in the vocabulary.

        Raises:
            None.
        """
        return self._utf_vocab_size

    def get_vocab(self):
        """
        Retrieves the vocabulary of the ByT5Tokenizer.

        Args:
            self (ByT5Tokenizer): An instance of the ByT5Tokenizer class.

        Returns:
            dict: A dictionary containing the vocabulary of the tokenizer.
                The keys are the tokens, and the values are the corresponding token IDs.

        Raises:
            None.

        Note:
            The vocabulary includes both the original vocabulary of the tokenizer and any additional tokens that have been added.

        Example:
            ```python
            >>> tokenizer = ByT5Tokenizer()
            >>> vocab = tokenizer.get_vocab()
            >>> print(vocab)
            {'<unk>': 0, '<pad>': 1, 'hello': 2, 'world': 3, ...}
            ```
        """
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    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
            )

        # normal case: some special tokens
        if token_ids_1 is None:
            return ([0] * len(token_ids_0)) + [1]
        return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

    def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
        """Do not add eos again if user already added it."""
        if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
                " eos tokens being added."
            )
            return token_ids
        return token_ids + [self.eos_token_id]

    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. ByT5 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.
        """
        eos = [self.eos_token_id]

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

    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. A sequence has the following format:

        - single sequence: `X </s>`
        - pair of sequences: `A </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.
        """
        token_ids_0 = self._add_eos_if_not_present(token_ids_0)
        if token_ids_1 is None:
            return token_ids_0
        token_ids_1 = self._add_eos_if_not_present(token_ids_1)
        return token_ids_0 + token_ids_1

    def _tokenize(self, text: str) -> List[str]:
        """Take as input a string and return a list of strings (tokens) for words/sub-words"""
        tokens = [chr(i) for i in text.encode("utf-8")]
        return tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        if len(token) != 1:
            token_id = None
        else:
            token_id = ord(token) + self.offset

        return token_id

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = chr(index - self.offset)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        bstring = b""
        for token in tokens:
            if token in self.added_tokens_decoder:
                tok_string = self.added_tokens_decoder[token].encode("utf-8")
            elif token in self.added_tokens_encoder:
                tok_string = token.encode("utf-8")
            else:
                tok_string = bytes([ord(token)])
            bstring += tok_string
        string = bstring.decode("utf-8", errors="ignore")
        return string

    # ByT5Tokenizer has no vocab file
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Saves the vocabulary of the ByT5Tokenizer instance to a file.

        Args:
            self (ByT5Tokenizer): The instance of the ByT5Tokenizer class.
            save_directory (str): The directory path where the vocabulary file will be saved.
            filename_prefix (Optional[str]): The prefix to be added to the filename (default: None).

        Returns:
            Tuple[str]: A tuple containing the absolute path of the saved vocabulary file.

        Raises:
            None.

        This method saves the vocabulary of the ByT5Tokenizer instance to a file in the specified save_directory.
        The filename of the vocabulary file is generated based on the provided filename_prefix, if any.
        If no filename_prefix is provided, the vocabulary file will be named using the default naming convention.
        The method returns a tuple containing the absolute path of the saved vocabulary file.
        """
        return ()

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.vocab_size property

Method to retrieve the vocabulary size of the ByT5Tokenizer instance.

PARAMETER	DESCRIPTION
self	ByT5Tokenizer instance. The self parameter refers to the instance of the ByT5Tokenizer class.

RETURNS	DESCRIPTION
int	The vocabulary size of the tokenizer. This value represents the total number of unique tokens in the vocabulary.

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.__init__(eos_token='</s>', unk_token='<unk>', pad_token='<pad>', extra_ids=125, additional_special_tokens=None, **kwargs)

This method initializes an instance of the ByT5Tokenizer class.

PARAMETER	DESCRIPTION
self	The instance of the ByT5Tokenizer class.
eos_token	The end-of-sequence token. Default is ''. TYPE: str DEFAULT: '</s>'
unk_token	The unknown token. Default is ''. TYPE: str DEFAULT: '<unk>'
pad_token	The padding token. Default is ''. TYPE: str DEFAULT: '<pad>'
extra_ids	The number of extra special tokens. Default is 125. TYPE: int DEFAULT: 125
additional_special_tokens	List of additional special tokens. Default is None. TYPE: list DEFAULT: None

RETURNS	DESCRIPTION
None	None.

RAISES	DESCRIPTION
ValueError	Raised if both extra_ids and additional_special_tokens are provided and the additional_special_tokens do not include all extra_ids tokens.

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

def __init__(
    self,
    eos_token="</s>",
    unk_token="<unk>",
    pad_token="<pad>",
    extra_ids=125,
    additional_special_tokens=None,
    **kwargs,
) -> None:
    """
    This method initializes an instance of the ByT5Tokenizer class.

    Args:
        self: The instance of the ByT5Tokenizer class.
        eos_token (str, optional): The end-of-sequence token. Default is '</s>'.
        unk_token (str, optional): The unknown token. Default is '<unk>'.
        pad_token (str, optional): The padding token. Default is '<pad>'.
        extra_ids (int, optional): The number of extra special tokens. Default is 125.
        additional_special_tokens (list, optional): List of additional special tokens. Default is None.

    Returns:
        None.

    Raises:
        ValueError: Raised if both extra_ids and additional_special_tokens are provided and
            the additional_special_tokens do not include all extra_ids tokens.
    """
    # Add extra_ids to the special token list
    if extra_ids > 0 and additional_special_tokens is None:
        additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
    elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0:
        # Check that we have the right number of extra_id special tokens
        extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
        if extra_tokens != extra_ids:
            raise ValueError(
                f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
                " extra_ids tokens"
            )

    pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token
    # we force left and right stripping for backward compatibility. The byt5tests depend on this.
    eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token
    unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token
    # unk token needs to be in the vocab with correct index
    self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token}
    self.offset = len(self._added_tokens_decoder)
    self._utf_vocab_size = 2**8  # utf is 8 bits
    super().__init__(
        eos_token=eos_token,
        unk_token=unk_token,
        pad_token=pad_token,
        extra_ids=0,
        additional_special_tokens=additional_special_tokens,  # TODO extra ids are not used :sweatywmile:
        **kwargs,
    )

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A sequence has the following format:

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

PARAMETER	DESCRIPTION
token_ids_0	List of IDs to which the special tokens will be added. TYPE: `List[int]`
token_ids_1	Optional second list of IDs for sequence pairs. TYPE: `List[int]`, *optional* DEFAULT: None

RETURNS	DESCRIPTION
List[int]	List[int]: List of input IDs with the appropriate special tokens.

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

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. A sequence has the following format:

    - single sequence: `X </s>`
    - pair of sequences: `A </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.
    """
    token_ids_0 = self._add_eos_if_not_present(token_ids_0)
    if token_ids_1 is None:
        return token_ids_0
    token_ids_1 = self._add_eos_if_not_present(token_ids_1)
    return token_ids_0 + token_ids_1

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.convert_tokens_to_string(tokens)

Converts a sequence of tokens (string) in a single string.

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

def convert_tokens_to_string(self, tokens):
    """Converts a sequence of tokens (string) in a single string."""
    bstring = b""
    for token in tokens:
        if token in self.added_tokens_decoder:
            tok_string = self.added_tokens_decoder[token].encode("utf-8")
        elif token in self.added_tokens_encoder:
            tok_string = token.encode("utf-8")
        else:
            tok_string = bytes([ord(token)])
        bstring += tok_string
    string = bstring.decode("utf-8", errors="ignore")
    return string

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)

Create a mask from the two sequences passed to be used in a sequence-pair classification task. ByT5 does not make use of token type ids, therefore a list of zeros is returned.

PARAMETER	DESCRIPTION
token_ids_0	List of IDs. TYPE: `List[int]`
token_ids_1	Optional second list of IDs for sequence pairs. TYPE: `List[int]`, *optional* DEFAULT: None

RETURNS	DESCRIPTION
List[int]	List[int]: List of zeros.

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

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. ByT5 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.
    """
    eos = [self.eos_token_id]

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

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False)

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.

PARAMETER	DESCRIPTION
token_ids_0	List of IDs. TYPE: `List[int]`
token_ids_1	Optional second list of IDs for sequence pairs. TYPE: `List[int]`, *optional* DEFAULT: None
already_has_special_tokens	Whether or not the token list is already formatted with special tokens for the model. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

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

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

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
        )

    # normal case: some special tokens
    if token_ids_1 is None:
        return ([0] * len(token_ids_0)) + [1]
    return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_vocab()

Retrieves the vocabulary of the ByT5Tokenizer.

PARAMETER	DESCRIPTION
self	An instance of the ByT5Tokenizer class. TYPE: ByT5Tokenizer

RETURNS	DESCRIPTION
dict	A dictionary containing the vocabulary of the tokenizer. The keys are the tokens, and the values are the corresponding token IDs.

Note

The vocabulary includes both the original vocabulary of the tokenizer and any additional tokens that have been added.

Example

>>> tokenizer = ByT5Tokenizer()
>>> vocab = tokenizer.get_vocab()
>>> print(vocab)
{'<unk>': 0, '<pad>': 1, 'hello': 2, 'world': 3, ...}

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

def get_vocab(self):
    """
    Retrieves the vocabulary of the ByT5Tokenizer.

    Args:
        self (ByT5Tokenizer): An instance of the ByT5Tokenizer class.

    Returns:
        dict: A dictionary containing the vocabulary of the tokenizer.
            The keys are the tokens, and the values are the corresponding token IDs.

    Raises:
        None.

    Note:
        The vocabulary includes both the original vocabulary of the tokenizer and any additional tokens that have been added.

    Example:
        ```python
        >>> tokenizer = ByT5Tokenizer()
        >>> vocab = tokenizer.get_vocab()
        >>> print(vocab)
        {'<unk>': 0, '<pad>': 1, 'hello': 2, 'world': 3, ...}
        ```
    """
    vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
    vocab.update(self.added_tokens_encoder)
    return vocab

mindnlp.transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.save_vocabulary(save_directory, filename_prefix=None)

Saves the vocabulary of the ByT5Tokenizer instance to a file.

PARAMETER	DESCRIPTION
self	The instance of the ByT5Tokenizer class. TYPE: ByT5Tokenizer
save_directory	The directory path where the vocabulary file will be saved. TYPE: str
filename_prefix	The prefix to be added to the filename (default: None). TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Tuple[str]	Tuple[str]: A tuple containing the absolute path of the saved vocabulary file.

This method saves the vocabulary of the ByT5Tokenizer instance to a file in the specified save_directory. The filename of the vocabulary file is generated based on the provided filename_prefix, if any. If no filename_prefix is provided, the vocabulary file will be named using the default naming convention. The method returns a tuple containing the absolute path of the saved vocabulary file.

Source code in mindnlp/transformers/models/byt5/tokenization_byt5.py

def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
    """
    Saves the vocabulary of the ByT5Tokenizer instance to a file.

    Args:
        self (ByT5Tokenizer): The instance of the ByT5Tokenizer class.
        save_directory (str): The directory path where the vocabulary file will be saved.
        filename_prefix (Optional[str]): The prefix to be added to the filename (default: None).

    Returns:
        Tuple[str]: A tuple containing the absolute path of the saved vocabulary file.

    Raises:
        None.

    This method saves the vocabulary of the ByT5Tokenizer instance to a file in the specified save_directory.
    The filename of the vocabulary file is generated based on the provided filename_prefix, if any.
    If no filename_prefix is provided, the vocabulary file will be named using the default naming convention.
    The method returns a tuple containing the absolute path of the saved vocabulary file.
    """
    return ()