tokenxxx.py

import json, re, unicodedata
from functools import lru_cache
import wget, os
from constants import GPT2_FOLDER, ENCODER_FILE, VOCAB_FILE, END_OF_TEXT_TOKEN
import nltk

@lru_cache()
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))

def get_pairs(word):
    pairs = set(); prev_char = word[0]
    for char in word[1:]: pairs.add((prev_char, char)); prev_char = char; return pairs

class Encoder:
    def __init__(self, encoder, bpe_merges, errors='replace', tokenize=None):
        self.encoder = encoder; self.decoder = {v:k for k,v in self.encoder.items()}; self.errors = errors
        self.byte_encoder = bytes_to_unicode(); self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        self.cache = {}; 
        if tokenize is None: self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\w+| ?[^\s\w]+|\s+(?!\S)|\s+""", re.UNICODE); self.tokenize = lambda text: re.findall(self.pat, text)
        else: self.tokenize = tokenize

    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); new_word.extend(word[i:j]); i = j
                except ValueError: new_word.extend(word[i:]); break
                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

    def encode(self, text):
        bpe_tokens = []; normalized_text = unicodedata.normalize('NFKC', text); normalized_text = ''.join(c for c in normalized_text if c.isascii() and c != '\t'); normalized_text = ''.join(c for c in normalized_text if not unicodedata.category(c).startswith('C'))
        for token in self.tokenize(normalized_text): token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8', errors='ignore')); bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def decode(self, tokens):
        text = ''.join([self.decoder[token] for token in tokens]); text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace')
        decoded_text = text.replace(" .", ".").replace(" ,", ",").replace(" '", "'").replace(" ?", "?").replace(" !", "!").replace(" :", ":").replace('\n', '<br>')
        sentences = nltk.sent_tokenize(decoded_text); return ' '.join(sentences).replace("<br>", "<br>\n")

def get_encoder_gpt2():
    encoder_path = os.path.join(GPT2_FOLDER, ENCODER_FILE); vocab_path = os.path.join(GPT2_FOLDER, VOCAB_FILE)
    if not os.path.exists(GPT2_FOLDER): os.makedirs(GPT2_FOLDER)
    if not os.path.exists(encoder_path): wget.download(ENCODER_URL, out=encoder_path)
    if not os.path.exists(vocab_path): wget.download(VOCAB_URL, out=vocab_path)
    with open(encoder_path, 'r') as f: encoder = json.load(f)
    with open(vocab_path, 'r', encoding="utf-8") as f: bpe_data = f.read()
    bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]; encoder_obj = Encoder(encoder=encoder, bpe_merges=bpe_merges)
    encoder_obj.encoder[END_OF_TEXT_TOKEN] = len(encoder_obj.encoder); encoder_obj.decoder[len(encoder_obj.decoder)] = END_OF_TEXT_TOKEN; return encoder_obj

def get_codegen_tokenizer_pure(vocab_file, merges_file):
    vocab = json.load(open(vocab_file)); merges = open(merges_file, 'r', encoding="utf-8").read().split('\n')[1:-1]; bpe_merges = [tuple(m.split()) for m in merges]
    byte_encoder = bytes_to_unicode(); byte_decoder = {v: k for k, v in byte_encoder.items()}
    tokenizer_regex = re.compile(r'''<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''); tokenize = lambda text: re.findall(tokenizer_regex, text)
    encoder_obj = Encoder(encoder=vocab, bpe_merges=bpe_merges, byte_encoder=byte_encoder, byte_decoder=byte_decoder, tokenize=tokenize); return encoder_obj

def codegen_tokenize(text, tokenizer): return tokenizer.encode(text)
def codegen_decode(tokens, tokenizer): return tokenizer.decode(tokens)