CSPON/nlp_tools/jupyter/qwen_agent/utils/tokenization_qwen.py

"""Tokenization classes for QWen."""

import base64
import unicodedata
from pathlib import Path
from typing import Collection, Dict, List, Set, Union

import tiktoken

from qwen_agent.log import logger

VOCAB_FILES_NAMES = {'vocab_file': 'qwen.tiktoken'}

PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
ENDOFTEXT = '<|endoftext|>'
IMSTART = '<|im_start|>'
IMEND = '<|im_end|>'
# as the default behavior is changed to allow special tokens in
# regular texts, the surface forms of special tokens need to be
# as different as possible to minimize the impact
EXTRAS = tuple((f'<|extra_{i}|>' for i in range(205)))
# changed to use actual index to avoid misconfiguration with vocabulary expansion
SPECIAL_START_ID = 151643
SPECIAL_TOKENS = tuple(enumerate(
    ((
        ENDOFTEXT,
        IMSTART,
        IMEND,
    ) + EXTRAS),
    start=SPECIAL_START_ID,
))
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)


def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
    with open(tiktoken_bpe_file, 'rb') as f:
        contents = f.read()
    return {
        base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)
    }


class QWenTokenizer:
    """QWen tokenizer."""

    vocab_files_names = VOCAB_FILES_NAMES

    def __init__(
        self,
        vocab_file=None,
        errors='replace',
        extra_vocab_file=None,
    ):
        if not vocab_file:
            vocab_file = VOCAB_FILES_NAMES['vocab_file']
        self._decode_use_source_tokenizer = False

        # how to handle errors in decoding UTF-8 byte sequences
        # use ignore if you are in streaming inference
        self.errors = errors

        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: Dict[bytes, int]
        self.special_tokens = {token: index for index, token in SPECIAL_TOKENS}

        # try load extra vocab from file
        if extra_vocab_file is not None:
            used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
            extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
            for token, index in extra_mergeable_ranks.items():
                if token in self.mergeable_ranks:
                    logger.info(f'extra token {token} exists, skipping')
                    continue
                if index in used_ids:
                    logger.info(f'the index {index} for extra token {token} exists, skipping')
                    continue
                self.mergeable_ranks[token] = index
            # the index may be sparse after this, but don't worry tiktoken.Encoding will handle this

        enc = tiktoken.Encoding(
            'Qwen',
            pat_str=PAT_STR,
            mergeable_ranks=self.mergeable_ranks,
            special_tokens=self.special_tokens,
        )
        assert len(self.mergeable_ranks) + len(
            self.special_tokens
        ) == enc.n_vocab, f'{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding'

        self.decoder = {v: k for k, v in self.mergeable_ranks.items()}  # type: dict[int, bytes|str]
        self.decoder.update({v: k for k, v in self.special_tokens.items()})

        self.tokenizer = enc  # type: tiktoken.Encoding

        self.eod_id = self.tokenizer.eot_token
        self.im_start_id = self.special_tokens[IMSTART]
        self.im_end_id = self.special_tokens[IMEND]

    def __getstate__(self):
        # for pickle lovers
        state = self.__dict__.copy()
        del state['tokenizer']
        return state

    def __setstate__(self, state):
        # tokenizer is not python native; don't pass it; rebuild it
        self.__dict__.update(state)
        enc = tiktoken.Encoding(
            'Qwen',
            pat_str=PAT_STR,
            mergeable_ranks=self.mergeable_ranks,
            special_tokens=self.special_tokens,
        )
        self.tokenizer = enc

    def __len__(self) -> int:
        return self.tokenizer.n_vocab

    def get_vocab(self) -> Dict[bytes, int]:
        return self.mergeable_ranks

    def convert_tokens_to_ids(self, tokens: Union[bytes, str, List[Union[bytes, str]]]) -> List[int]:
        ids = []
        if isinstance(tokens, (str, bytes)):
            if tokens in self.special_tokens:
                return self.special_tokens[tokens]
            else:
                return self.mergeable_ranks.get(tokens)
        for token in tokens:
            if token in self.special_tokens:
                ids.append(self.special_tokens[token])
            else:
                ids.append(self.mergeable_ranks.get(token))
        return ids

    def tokenize(
            self,
            text: str,
            allowed_special: Union[Set, str] = 'all',
            disallowed_special: Union[Collection, str] = (),
    ) -> List[Union[bytes, str]]:
        """
        Converts a string in a sequence of tokens.

        Args:
            text (`str`):
                The sequence to be encoded.
            allowed_special (`Literal["all"]` or `set`):
                The surface forms of the tokens to be encoded as special tokens in regular texts.
                Default to "all".
            disallowed_special (`Literal["all"]` or `Collection`):
                The surface forms of the tokens that should not be in regular texts and trigger errors.
                Default to an empty tuple.

        Returns:
            `List[bytes|str]`: The list of tokens.
        """
        tokens = []
        text = unicodedata.normalize('NFC', text)

        # this implementation takes a detour: text -> token id -> token surface forms
        for t in self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special):
            tokens.append(self.decoder[t])
        return tokens

    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
        """
        Converts a sequence of tokens in a single string.
        """
        text = ''
        temp = b''
        for t in tokens:
            if isinstance(t, str):
                if temp:
                    text += temp.decode('utf-8', errors=self.errors)
                    temp = b''
                text += t
            elif isinstance(t, bytes):
                temp += t
            else:
                raise TypeError('token should only be of type types or str')
        if temp:
            text += temp.decode('utf-8', errors=self.errors)
        return text

    @property
    def vocab_size(self):
        return self.tokenizer.n_vocab

    def _decode(
        self,
        token_ids: Union[int, List[int]],
        skip_special_tokens: bool = False,
        errors: str = None,
    ) -> str:
        if isinstance(token_ids, int):
            token_ids = [token_ids]
        if skip_special_tokens:
            token_ids = [i for i in token_ids if i < self.eod_id]
        return self.tokenizer.decode(token_ids, errors=errors or self.errors)

    def encode(self, text: str) -> List[int]:
        return self.convert_tokens_to_ids(self.tokenize(text))

    def count_tokens(self, text: str) -> int:
        return len(self.tokenize(text))

    def truncate(self, text: str, max_token: int, start_token: int = 0) -> str:
        token_list = self.tokenize(text)
        token_list = token_list[start_token:min(len(token_list), start_token + max_token)]
        return self.convert_tokens_to_string(token_list)


tokenizer = QWenTokenizer(Path(__file__).resolve().parent / 'qwen.tiktoken')


def count_tokens(text: str) -> int:
    return tokenizer.count_tokens(text)