.save/filters_epub.py

import json, sys, gzip, glob, lzma
import re, unicodedata

try:
  sys.argv[2]
  logging = True
except:
  logging = False

import fasttext
fasttext_model = fasttext.load_model("fasttext_epub_vs_cc.bin")
def fasttext_predict(text) -> tuple:
    result = fasttext_model.predict(text)
    label = result[0][0]
    prob = result[1][0]
    return label, prob

syms = set("khum toap chuyn mien khuech khun nun lin dong buan kay nhuyen ruet duet khoap lum nhit khoanh kheng lieng xuap thia luot truynh khoay thy quap vien chieng duya luech mai moam nguyech chuych phuan hach xuyen chum phoeng duyu giech suya loam choam bich biu phuya trai doa khim xom phec cunh buch quep rien meu muoi lunh uap penh xach chuang triec luo khi soi choo muu phun chuen meng oai nghenh toen que troam tap gheu cheu chuam rao khuynh nec phu ruom xuech thoac tuon hech bien ghi duyp ping khoi bap koai suyp thoam chieu gienh noe sap cau loap chay day pinh chuo cieu goao duon min nuyn mia nen nang thoao rech nam troanh lit toong ngha thuonh toac hooc tuat genh cup phia linh chich nhoai giua meo ngoat quyu chic xeu lep vic kat chuyech niet soeng khe hao nganh nhuon thau nuoch chuyenh tup tac boet piep vot choanh goam xec tuay doc dac bech oeo dang hoach loen set goon pooc noon toat phuyn nia nghau uyt kong nhoem kich bic xuyec coang nget thuam tuem khuyenh khoeo lien ging boo chot nghin voang man sut koa che quan uang moon ung hien nghit sun hue kien quai ghec hiu puya kuan ngoac goan suen thuy luenh tre dec moe thoo men ning quac soat nhim nuang nheo ngit ngheo nguyet bup doan pha nuot mach khoang bay thup nghienh troang hoang tem guyet nhiep phoai nung khoe hoai phuen kec oach ben koam ghech vuo beo choach phoanh quem thao giec giuong pic treng eng ngoe xoc huet khiem xoep roon nguen huonh xoe chinh ghet duang bum vich ngu niu phoem guo hiep chep ghip vuat beu tric chue kuop chat rim phoc goap lai piu noang bep moang tron luyp lia nge xenh chuet try soem duong guong trua huy vum ghem mon quing vuong dut dop giuoc hap viem nem vue luep ruem piet thiet tunh soc ron quiet khuam doe hoanh nghuoi cuya giai nieng giuc nhut phau phiec xuong guang pim puat sit vep pin rut nghinh rom nheng troeo diem kuoi xoang tai theu suou boch uyn van pen doat luc pang reo senh vunh sach liec quyenh phuyu bun huynh troi deng ret ghang xuom phy chiet quao sen pau tuep guech lac nghoanh giue booc roach toai doo nhuang khui nuyt hiem nhap vooc rang khuyn puong thoay mom vuya kao ceng minh quyet ram xien nhac chooc gen nau ngum guoi ghup thoat voon quong poac trop vuyet doang tuo soe vai buc nuat giom ren henh kuat nghom trinh duonh nac tren nhiet pong num pac guom loach chac ghep gioan luan poe son tet riep doen huon nhuc tinh muom cheng khuay nhin heo leo thec poc yen koay lonh gioay hich nguo phuoc xai mooc loanh xieng dim yem ruou vieng liet tuyt duen nay nguenh huay khet luong ghit uot dien truen dao hec pap poem nguan hoo gem conh phoang lop xuyet ong huenh nguong coo sum ghap thing nhien menh xem phim ghim nghac ruech kan nhet tuya hang quu rung chach xuyt uem muou noo phet tuyn chy nghang toeng hem tic tay piem pui but hoch nghe trum gioen uenh rum tat giao chuyec giuu khoeng nguyem ngieu much kiep nhuem tuang soach coan boi khuong gung bon ming khot toanh xia guc gium quia nhon gin phuyen thuynh phing oanh khoet choong khen oay choen trut nghut guych 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ngap dinh guya muych roac koen koonh riec nhuynh roap kau nhia soam poa quit oac giuot ponh net ginh bach kuo uon toach that hoem suot vuang ngup phanh ruu chuan pham kiu hup suoc xeng ket khau xooc doanh nhec hoong cit muon dun goi khay mem pup rach moen rich rap ngach duoc uyu dia thuou buya com suam pech guym sich thuang nghim phao vit chenh ring voong nhich phech chiu nghai peng noet quip loo ghieng noem toem khich khuya tham nghuyet tum thua theo nuyet tuc ghach thoa coen thu thuu moom miec khoong khuy kem riem thue tuyet thinh oep chech than khoan looc ngham chuot oang soay cia duy thoach hunh loeng nap poat pep thuom nhuat roc room muen buy phuynh nut hoa moay trun toam khiep poi troe dat phuong phuon giuo puot tuom mang xoach biec mit gom nhinh tram noac phin xin quoanh ruo troac lom voc xuat voam ngac phenh ngenh chin nhuech non muay chuyet ling nguyu chip thip buyn toec ngat xuyu trong trach doai kech dum quoai roanh giam thit tec roe huyt tuyech goac xon boc ghat gien mech thiu nhuam khiet giun sanh theng nghet nhuay ghe choa xay oan cuat pon nhuyn quuyen xeo xuoi ban mau miep nhong truonh chon phuoi quyt too gup nhuyu hen kum thin giot truoi muet troet hiet nhuy nuynh ghich khup trao trunh goo huoc poan giep uam quuay khong phue niep khin rau ghinh nhooc uynh toan veu phe buet ngon nit tuech suyt gat suo xuu nhau reu chap".split())
def tieng_viet_thuong_khong_dau(s):
    s = re.sub(u'Đ', 'D', s) # ko cần nữa vì text đã ở dạng lower
    s = re.sub(u'đ', 'd', s)
    s = unicodedata.normalize('NFKD', s).encode('ASCII', 'ignore')
    return s.decode("utf-8")

bad_words = """

bú cu, bú cặc, bú lồn, bú lol, bú lon, bú cac, bú cak, bú cax, clip sêx,

bú buoi, bú mút, chịch kiểu chó, chịch lỗ đít, chịch lén, chịch tập thể, húp lol, húp lon, húp sò, 

nung lol, nung lon, nug lon, nung cac, nung buoi, thèm dit, thèm lon, thèm lol, thèm buoi, thèm dit, 

thèm chich, thèm chịch, thèm cax, thèm cak, móc lol, móc lon, lồn múp, nứng lồn, buscu, lol non, 

bướm non, vú to, vú bự, lon to, phim sex, phim xex, phim xxx, phim dâm, hiep dam, làm tình, bú cặc,

yeusex, sexloasn, xem lồn, địt mũi, buồi địt, sex asian, Sex lau xanh, coi quan ao, fuck sillicon toy

""".strip().split(",")
bad_words = [ x.strip().replace(" ","\\s*") for x in bad_words]
bad_words = list(set(bad_words)) # uniq
tmp = "|".join(bad_words)
bad_words_re = rf"[^\S]({tmp})[^\S]"
def bad_words_count(t):
  return len(re.findall(bad_words_re, t)) 

def sym_filter(t, ratio=0.6):
  t = tieng_viet_thuong_khong_dau(t)
  tokens = re.split(r'[^a-z]+', t)
  sym_count = 0
  for tk in tokens:
    if tk in syms: sym_count += 1
  if sym_count / len(tokens) < ratio:
    return t
  else: return True


# https://github.com/CarperAI/squeakily/blob/main/squeakily/filter.py
from collections import Counter
import numpy as np
def _char_rep_ratio(

    doc: str,  # document to be analyzed

    char_rep_len: int,  # length of character repetition

) -> float:
    """Returns the ratio of character repetitions in a document."""
    def calc_ngrams(doc, n):
        char_ngrams = [doc[i : i + n] for i in range(len(doc) - n + 1)]
        freq_char_ngrams = Counter(char_ngrams)
        return freq_char_ngrams

    freq_char_ngrams = calc_ngrams(doc, char_rep_len)
    if len(freq_char_ngrams) == 0:
        return 0
    freq_char_ngrams = list(freq_char_ngrams.values())
    freq_char_ngrams = sorted(freq_char_ngrams, reverse=True)
    val_one = len([el for el in freq_char_ngrams if el == 1])
    num_rep_char_ngrams = min(
        int(np.sqrt(len(freq_char_ngrams))),
        len(freq_char_ngrams) - val_one,
    )
    char_rep_ratio = sum(freq_char_ngrams[:num_rep_char_ngrams]) / sum(freq_char_ngrams)
    return char_rep_ratio


def check_char_repetition(

    document,  # document to be analyzed

    char_repetition_len=10,  # length of character repetition

    char_repetition_threshold=0.02,  # threshold for character repetition

    dry_run=False,  # if True, returns the ratio of character repetition

) -> bool:  # returns True if document is below threshold
    """Checks if the document is below the character repetition threshold."""
    char_rep_ratio = _char_rep_ratio(document, char_repetition_len)
    if dry_run: return char_rep_ratio
    else: return char_rep_ratio <= char_repetition_threshold


###########################

input_files = sys.argv[1].split(",")
if input_files[0] == "jsonl":
  import glob
  input_files = glob.glob("jsonl/*")

for input_file in input_files:
  file_prefix = input_file.split(".jsonl.lzma")[0]
  output_file = f"{file_prefix}_filtered.jsonl.gz"
  output_file = output_file.replace("jsonl/", "filtered/")

  print(f"{input_file} => {output_file}")
  fo = gzip.open(output_file, "wt")
  if logging:
    fb = open(f"{file_prefix}_bad.txt", "wt")
    fl = open(f"{file_prefix}_low.txt", "wt")
    fa = open(f"{file_prefix}_avg.txt", "wt")
    fr = open(f"{file_prefix}_rep.txt", "wt")

  with lzma.open(input_file, "rt") as f:
    count = bad_count = short_count = low_count = avg_count = rep_count = 0
    for line in f:
      count += 1
      d = json.loads(line)

      # Lọc theo độ dài văn bản
      if len(d["text"]) < 600:
        short_count += 1
        continue

      low_text = d["text"].lower()
      # Lọc theo bad words
      if bad_words_count(low_text) > 5:
        bad_count += 1
        if logging: fb.write(low_text + "\n\n\n- - - - - - - -\n\n\n")
        continue

      # Lọc theo tỉ lệ âm tiết tiếng Việt
      t = sym_filter(low_text)
      if t != True:
        low_count += 1
        if logging: fl.write(low_text + "\n\n\n- - - - - - - -\n\n\n")
        continue

      # Lọc theo độ lặp lại của ký tự
      if check_char_repetition(low_text):
        rep_count += 1
        if logging: fr.write(low_text + "\n\n\n- - - - - - - -\n\n\n")
        continue

      # Lọc theo good vs c4 classifier
      t = d["text"].replace("\n", "  ")
      label, score = fasttext_predict(t)
      if label != "__label__epub" or score < 0.6:
        avg_count += 1
        if logging: fa.write(f"{label} {t}\n")
        continue

      ss = json.dumps({"text": d["text"]}, ensure_ascii=False)
      fo.write(ss + "\n")

  fo.close()
  if logging:
    fb.close()
    fl.close()
    fa.close()
    fr.close()


  print(f"total {count}, short {short_count}, bad {bad_count}, \

low {low_count}, rep {rep_count}, avg {avg_count}")

  short_percent = "{0:.2%}".format(short_count / count); count -= short_count
  bad_percent = "{0:.2%}".format(bad_count / count); count -= bad_count
  low_percent = "{0:.2%}".format(low_count / count); count -= low_count
  rep_percent = "{0:.2%}".format(rep_count / count); count -= rep_count
  avg_percent = "{0:.2%}".format(avg_count / count); count -= avg_count

  print(f"remain {count}, short {short_percent}, bad {bad_percent}, \

low {low_percent}, rep {rep_percent}, avg {avg_percent}")