"""
special_symbols: https://github.com/google/sentencepiece/blob/master/doc/special_symbols.md
emoji:
"""

import sys


# 来自 https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/tokenizer/gpt2_tokenization.py#L55
# 啥意思？
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    _chr = unichr if sys.version_info[0] == 2 else chr
    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))

aa = bytes_to_unicode()
print(aa)