character_util.py

"""
TODO:
1. add more language
2. check space count of bert
3. add token_impl
4.
"""
import os
import json
import numpy as np
import pandas as pd
from collections import Counter, defaultdict
from vocab import tokenizer_factory
from typing import Optional, Union, Literal
from utils.log_util import logger
from utils.text_util import contains_digit, get_space_count
from utils.lang_util import detect_language_by_unicode, language_ranges

CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))

default_columns = ["digit", "zh"]

def _to_unicode(text):
    return ''.join(r'\u{:04X}'.format(ord(chr)) for chr in text)


def _get_coding_length(tokenizer, vocab, filter=None):
    """
    oov character may be tokenized into more than one token.
    """
    all_length = []
    for word in vocab:
        if len(word) > 1:
            continue
        if filter is not None and filter(word):
            continue
        try:
            tokens = tokenizer.encode(word)
        except Exception as e:
            print(e)

        all_length.append(len(tokens))
        # if len(tokens.ids) > 1:
        # if len(tokens) > 3:
        #     print(word, tokens)

    dist_length = Counter(all_length)
    mean_length = round(sum(all_length) / len(all_length), 2)
    return dist_length, mean_length


cache = {}


def _dist(token_lens):
    """
    :param token_lens:
    :return: min,median,max of token_lens
    """
    if not token_lens:
        return "-"
    return f"{min(token_lens)},{round(np.median(token_lens))},{max(token_lens)}"


def iter_vocab(
        tokenizer_name: str,
        from_cache: bool = True,
        cache_dir: str = "stats",
) -> Union[pd.DataFrame, dict]:
    """
    :param tokenizer_name:
    :param from_cache:
    :param cache_dir:
    :return:
    """
    tokenizer_config = tokenizer_factory.get_tokenizer_config(tokenizer_name)

    cache_dir = os.path.join(CURRENT_DIR, cache_dir)
    os.makedirs(cache_dir, exist_ok=True)

    # load from cache
    cache_path = os.path.join(cache_dir, "character_stats.json")
    if not cache and os.path.exists(cache_path):
        with open(cache_path, "r", encoding="utf-8") as f_tmp:
            cache.update(json.load(f_tmp))
    if from_cache and tokenizer_name in cache:
        # logger.info(f"load {tokenizer_config.name_or_path} from cache")
        return cache[tokenizer_name]

    tokenizer = tokenizer_factory.get_tokenizer(tokenizer_name)

    tokens_by_lang = {lang[1]: [] for lang in language_ranges.keys()}
    digit_tokens = []
    space_tokens = []
    byte_tokens = []

    buffer = []
    for token_id in range(tokenizer.vocab_size):
        # for token_id in tokenizer.get_vocab():
        # for token_id in range(len(tokenizer)):
        decode_str = tokenizer.decode([token_id], skip_special_tokens=False)
        token = tokenizer.convert_ids_to_tokens([token_id], skip_special_tokens=False)[0]
        tags = []
        if token is None:  # 有些词典有空的id（不连续）
            continue
        if isinstance(token, bytes):
            token = token.decode("utf-8", errors="ignore")

        if hasattr(tokenizer, "sp_model"):  # 基于 sentencepiece 包
            if tokenizer.sp_model.is_byte(token_id):
                tags.append("is_byte")
                byte_tokens.append(token)

        language_tags = detect_language_by_unicode(decode_str)
        for language in language_tags:
            tokens_by_lang[language[1]].append(decode_str)

        if contains_digit(decode_str):
            tags.append("digit")
            digit_tokens.append(decode_str)

        space_count = get_space_count(decode_str)
        if space_count > 0:
            space_tokens.append(decode_str)

        buffer.append(json.dumps(
            {
                "id": token_id,
                "token": token,
                "token_decode": decode_str,
                "token_dumps": json.dumps(token),
                "token_unicode": _to_unicode(token),
                "token_len": len(decode_str),
            },
            ensure_ascii=False) + "\n")

    result = {
        "tokenizer": tokenizer_factory.get_name_with_hyperlink(tokenizer_name),
        "organization": tokenizer_config.org,
        # "impl": str(tokenizer.__class__),
        # "vocab_size-": tokenizer.vocab_size,  # vocab_size_without_added_token
        "vocab_size": len(tokenizer),

        # "中文汉字编码长度均值": mean_length,   # 不用统计，因为字典包含中文字符多，一般就意味着 中文汉字编码长度短。
        # "中文汉字编码长度分布": json.dumps(dist_length),

        "num(digit)": len(digit_tokens),
        "len(digit)": _dist([len(token) for token in digit_tokens]),
        "num(space)": len(space_tokens),
        "len(space)": _dist([len(token) for token in space_tokens]),

        # "num(byte)": len(byte_tokens)
    }

    for lang, tokens in tokens_by_lang.items():
        result[f"num({lang})"] = len(tokens)
        result["len(" + lang + ")"] = _dist([len(token) for token in tokens])

    out_path = os.path.join(cache_dir, f"iter_vocab/{tokenizer_name.replace('/', '_')}.vocab.jsonl")
    with open(out_path, "w", encoding="utf-8") as f_out:
        for line in buffer:
            f_out.write(line)
    len_before = len(cache)
    cache[tokenizer_name] = result
    len_after = len(cache)
    logger.info(f"saving {tokenizer_name} to memory and file cache: {len_before}->{len_after}")
    with open(cache_path, "w", encoding="utf-8") as f_out:
        f_out.write(json.dumps(cache, ensure_ascii=False, indent=2))
    return result


def to_dataframe(stats, columns):
    table = []
    for stat in stats.values():
        filtered_stat = {}
        for k, v in stat.items():
            if not k.startswith("num") and not k.startswith("len"):
                filtered_stat[k] = v
            if any(column in k for column in columns):
                k = k.replace("ja-kana", "kana")
                filtered_stat[k] = v
        table.append(filtered_stat)
    df = pd.DataFrame(table)
    return df


def get_character_table(
        tokenizer_filter: Optional[str] = None,
        columns: Optional[list] = None,
        return_type: Optional[Literal["dict", "dataframe"]] = "dataframe"
) -> Union[pd.DataFrame, dict]:
    """
    """
    logger.info(f"columns: {columns}, tokenizer_filter: {tokenizer_filter}")
    stats = {}
    if columns is None:
        columns = default_columns
    if tokenizer_filter is not None:
        tokenizer_names = [tokenizer_config.name_or_path for tokenizer_config in tokenizer_factory.all_tokenizer_configs
                           if tokenizer_filter.lower() in tokenizer_config.name_or_path.lower()]
    else:
        tokenizer_names = tokenizer_factory.all_tokenizer_names

    for tokenizer_name in tokenizer_names:
        stat = iter_vocab(tokenizer_name)
        stats[tokenizer_name] = stat

    if return_type == "dataframe":
        stats = to_dataframe(stats, columns)
    return stats


if __name__ == "__main__":
    # aa = get_character_table(tokenizer_filter="baichuan")
    df = get_character_table()
    logger.info(f"\n{df.to_markdown(index=False)}")