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vllm.tokenizers.protocol

TokenizerLike

Bases: Protocol

Source code in vllm/tokenizers/protocol.py 
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class TokenizerLike(Protocol):
    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: str,
        /,
        *,
        revision: str | None = None,
    ) -> Self:
        raise NotImplementedError

    @property
    def all_special_tokens(self) -> list[str]:
        raise NotImplementedError

    @property
    def all_special_ids(self) -> list[int]:
        raise NotImplementedError

    @property
    def bos_token_id(self) -> int:
        raise NotImplementedError

    @property
    def eos_token_id(self) -> int:
        raise NotImplementedError

    @property
    def is_fast(self) -> bool:
        raise NotImplementedError

    @property
    def vocab_size(self) -> int:
        raise NotImplementedError

    @property
    def max_token_id(self) -> int:
        raise NotImplementedError

    @property
    def truncation_side(self) -> str:
        raise NotImplementedError

    def __hash__(self) -> int:
        return hash(id(self))

    def __len__(self) -> int:
        return self.vocab_size

    def __call__(
        self,
        text: str | list[str] | list[int],
        text_pair: str | None = None,
        add_special_tokens: bool = False,
        truncation: bool = False,
        max_length: int | None = None,
    ):
        raise NotImplementedError

    def get_vocab(self) -> dict[str, int]:
        raise NotImplementedError

    def get_added_vocab(self) -> dict[str, int]:
        raise NotImplementedError

    def encode(
        self,
        text: str,
        truncation: bool | None = None,
        max_length: int | None = None,
        add_special_tokens: bool | None = None,
    ) -> list[int]:
        raise NotImplementedError

    def apply_chat_template(
        self,
        messages: list["ChatCompletionMessageParam"],
        tools: list[dict[str, Any]] | None = None,
        **kwargs,
    ) -> list[int]:
        raise NotImplementedError

    def convert_tokens_to_string(self, tokens: list[str]) -> str:
        raise NotImplementedError

    def decode(self, ids: list[int] | int, skip_special_tokens: bool = True) -> str:
        raise NotImplementedError

    def convert_ids_to_tokens(
        self,
        ids: list[int],
        skip_special_tokens: bool = True,
    ) -> list[str]:
        raise NotImplementedError

all_special_ids property 

all_special_ids: list[int]

all_special_tokens property 

all_special_tokens: list[str]

bos_token_id property 

bos_token_id: int

eos_token_id property 

eos_token_id: int

is_fast property 

is_fast: bool

max_token_id property 

max_token_id: int

truncation_side property 

truncation_side: str

vocab_size property 

vocab_size: int

__call__ 

__call__(
    text: str | list[str] | list[int],
    text_pair: str | None = None,
    add_special_tokens: bool = False,
    truncation: bool = False,
    max_length: int | None = None,
)
Source code in vllm/tokenizers/protocol.py 
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def __call__(
    self,
    text: str | list[str] | list[int],
    text_pair: str | None = None,
    add_special_tokens: bool = False,
    truncation: bool = False,
    max_length: int | None = None,
):
    raise NotImplementedError

__hash__ 

__hash__() -> int
Source code in vllm/tokenizers/protocol.py 
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def __hash__(self) -> int:
    return hash(id(self))

__len__ 

__len__() -> int
Source code in vllm/tokenizers/protocol.py 
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def __len__(self) -> int:
    return self.vocab_size

apply_chat_template 

apply_chat_template(
    messages: list[ChatCompletionMessageParam],
    tools: list[dict[str, Any]] | None = None,
    **kwargs,
) -> list[int]
Source code in vllm/tokenizers/protocol.py 
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def apply_chat_template(
    self,
    messages: list["ChatCompletionMessageParam"],
    tools: list[dict[str, Any]] | None = None,
    **kwargs,
) -> list[int]:
    raise NotImplementedError

convert_ids_to_tokens 

convert_ids_to_tokens(
    ids: list[int], skip_special_tokens: bool = True
) -> list[str]
Source code in vllm/tokenizers/protocol.py 
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def convert_ids_to_tokens(
    self,
    ids: list[int],
    skip_special_tokens: bool = True,
) -> list[str]:
    raise NotImplementedError

convert_tokens_to_string 

convert_tokens_to_string(tokens: list[str]) -> str
Source code in vllm/tokenizers/protocol.py 
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def convert_tokens_to_string(self, tokens: list[str]) -> str:
    raise NotImplementedError

decode 

decode(
    ids: list[int] | int, skip_special_tokens: bool = True
) -> str
Source code in vllm/tokenizers/protocol.py 
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def decode(self, ids: list[int] | int, skip_special_tokens: bool = True) -> str:
    raise NotImplementedError

encode 

encode(
    text: str,
    truncation: bool | None = None,
    max_length: int | None = None,
    add_special_tokens: bool | None = None,
) -> list[int]
Source code in vllm/tokenizers/protocol.py 
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def encode(
    self,
    text: str,
    truncation: bool | None = None,
    max_length: int | None = None,
    add_special_tokens: bool | None = None,
) -> list[int]:
    raise NotImplementedError

from_pretrained classmethod 

from_pretrained(
    pretrained_model_name_or_path: str,
    /,
    *,
    revision: str | None = None,
) -> Self
Source code in vllm/tokenizers/protocol.py 
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@classmethod
def from_pretrained(
    cls,
    pretrained_model_name_or_path: str,
    /,
    *,
    revision: str | None = None,
) -> Self:
    raise NotImplementedError

get_added_vocab 

get_added_vocab() -> dict[str, int]
Source code in vllm/tokenizers/protocol.py 
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def get_added_vocab(self) -> dict[str, int]:
    raise NotImplementedError

get_vocab 

get_vocab() -> dict[str, int]
Source code in vllm/tokenizers/protocol.py 
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def get_vocab(self) -> dict[str, int]:
    raise NotImplementedError