tokenization_lingxi.py

"""Tokenization classes for Lingxi."""

import json
import os
import unicodedata
from functools import lru_cache
from typing import Optional, Tuple

import regex as re

from transformers import AddedToken, PreTrainedTokenizer
from transformers.utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}


MAX_MODEL_INPUT_SIZES = {"lingxi/lingxi-tokenizer": 1024}

PRETOKENIZE_REGEX = 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+"""


@lru_cache()
# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
def bytes_to_unicode():
    
    bs = (
        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
    )
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs
def get_pairs(word):
    """
    Return set of symbol pairs in a word.

    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


class LingxiTokenizer(PreTrainedTokenizer):
    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        merges_file,
        errors="replace",
        unk_token="<|endoftext|>",
        bos_token=None,
        eos_token="<|im_end|>",
        pad_token="<|endoftext|>",
        clean_up_tokenization_spaces=False,
        split_special_tokens=False,
        **kwargs,
    ):
        bos_token = (
            AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
            if isinstance(bos_token, str)
            else bos_token
        )
        eos_token = (
            AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
            if isinstance(eos_token, str)
            else eos_token
        )
        unk_token = (
            AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
            if isinstance(unk_token, str)
            else unk_token
        )
        pad_token = (
            AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
            if isinstance(pad_token, str)
            else pad_token
        )

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.errors = errors  # how to handle errors in decoding
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        bpe_merges = []
        with open(merges_file, encoding="utf-8") as merges_handle:
            for i, line in enumerate(merges_handle):
                line = line.strip()
                if (i == 0 and line.startswith("#version:")) or not line:
                    continue
                bpe_merges.append(tuple(line.split()))
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        # NOTE: the cache can grow without bound and will get really large for long running processes
        # (esp. for texts of language that do not use space between word, e.g. Chinese); technically
        # not a memory leak but appears as one.
        # GPT2Tokenizer has the same problem, so let's be consistent.
        self.cache = {}

        self.pat = re.compile(PRETOKENIZE_REGEX)

        if kwargs.get("add_prefix_space", False):
            logger.warning_once(
                f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
            )

        super().__init__(
            errors=errors,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            unk_token=unk_token,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            split_special_tokens=split_special_tokens,
            **kwargs,
        )

    @property
    def vocab_size(self) -> int:
        return len(self.encoder)

    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab
    def get_vocab(self):
        return dict(self.encoder, **self.added_tokens_encoder)

    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
    @lru_cache(maxsize=100)  # 设置缓存大小为100
    def bpe(self, token):
        # if token in self.cache:
        #     return self.cache[token]
        word = tuple(token)
        pairs = get_pairs(word)

        if not pairs:
            return token

        while True:
            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                except ValueError:
                    new_word.extend(word[i:])
                    break
                else:
                    new_word.extend(word[i:j])
                    i = j

                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = " ".join(word)
        # self.cache[token] = word
        return word

    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize
    def _tokenize(self, text):
        """Tokenize a string."""
        bpe_tokens = []
        for token in re.findall(self.pat, text):
            token = "".join(
                self.byte_encoder[b] for b in token.encode("utf-8")
            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
        return bpe_tokens

    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id
    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.decoder.get(index)

    
    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        text = "".join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
        return text

    def decode(
        self,
        token_ids,
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: Optional[bool] = False,
        spaces_between_special_tokens: bool = False,
        **kwargs,
    ) -> str:
        
        return super().decode(
            token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            spaces_between_special_tokens=spaces_between_special_tokens,
            **kwargs,
        )

    
    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
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )
        merge_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")

        index = 0
        with open(merge_file, "w", encoding="utf-8") as writer:
            writer.write("#version: 0.2\n")
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                        " Please check that the tokenizer is not corrupted!"
                    )
                    index = token_index
                writer.write(" ".join(bpe_tokens) + "\n")
                index += 1

        return vocab_file, merge_file

    def prepare_for_tokenization(self, text, **kwargs):
        text = unicodedata.normalize("NFC", text)
        return (text, kwargs)