#! /usr/bin/python3
src="KoichiYasuoka/modernbert-base-japanese-wikipedia-upos"
tgt="KoichiYasuoka/modernbert-base-japanese-wikipedia-ud-triangular"
url="https://github.com/UniversalDependencies/UD_Japanese-GSDLUW"
import os
d=os.path.basename(url)
os.system("test -d "+d+" || git clone --depth=1 "+url)
os.system("for F in train dev test ; do cp "+d+"/*-$F.conllu $F.conllu ; done")
class UDTriangularDataset(object):
  def __init__(self,conllu,tokenizer):
    self.conllu=open(conllu,"r",encoding="utf-8")
    self.tokenizer=tokenizer
    self.seeks=[0]
    label=set(["SYM","X"])
    dep=set(["X|r-goeswith"])
    s=self.conllu.readline()
    while s!="":
      if s=="\n":
        self.seeks.append(self.conllu.tell())
      elif s.startswith("# text ="):
        t=s[8:].strip()
      else:
        w=s.split("\t")
        if len(w)==10:
          if w[0].isdecimal():
            p=w[3] if w[5]=="_" else w[3]+"|"+w[5]
            label.add(p)
            dep.add(p+("|" if w[6]=="0" else "|l-" if int(w[0])<int(w[6]) else "|r-")+w[7])
      s=self.conllu.readline()
    lid={l:i for i,l in enumerate(sorted(label))}
    for i,d in enumerate(sorted(dep),len(lid)):
      lid[d]=i
    self.label2id=lid
  def __call__(*args):
    lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
    for t in args:
      t.label2id=lid
    return lid
  def __del__(self):
    self.conllu.close()
  __len__=lambda self:len(self.seeks)-1
  def __getitem__(self,i):
    s=self.seeks[i]
    self.conllu.seek(s)
    c,t=[],[""]
    while t[0]!="\n":
      t=self.conllu.readline().split("\t") 
      if len(t)==10 and t[0].isdecimal():
        c.append(t)
    v=self.tokenizer([t[1] for t in c],add_special_tokens=False)["input_ids"]
    for i in range(len(v)-1,-1,-1):
      for j in range(1,len(v[i])):
        c.insert(i+1,[c[i][0],"_","_","X","_","_",c[i][0],"goeswith","_","_"])
    y=["0"]+[t[0] for t in c]
    h=[i if t[6]=="0" else y.index(t[6]) for i,t in enumerate(c,1)]
    p=[t[3] if t[5]=="_" else t[3]+"|"+t[5] for t in c]
    d=[t[7] if t[6]=="0" else "l-"+t[7] if int(t[0])<int(t[6]) else "r-"+t[7] for t in c]
    v=sum(v,[])
    ids=[self.tokenizer.cls_token_id]
    upos=["SYM"]
    for i,k in enumerate(v):
      ids.append(k)
      upos.append(p[i]+"|"+d[i] if h[i]==i+1 else p[i])
      for j in range(i+1,len(v)):
        ids.append(v[j])
        upos.append(p[j]+"|"+d[j] if h[j]==i+1 else p[i]+"|"+d[i] if h[i]==j+1 else p[j])
      ids.append(self.tokenizer.sep_token_id)
      upos.append("SYM")
    return {"input_ids":ids,"labels":[self.label2id[p] for p in upos]}
from transformers import AutoTokenizer,AutoConfig,AutoModelForTokenClassification,DataCollatorForTokenClassification,TrainingArguments,Trainer
tkz=AutoTokenizer.from_pretrained(src)
trainDS=UDTriangularDataset("train.conllu",tkz)
devDS=UDTriangularDataset("dev.conllu",tkz)
testDS=UDTriangularDataset("test.conllu",tkz)
lid=trainDS(devDS,testDS)
cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True,trust_remote_code=True)
mdl=AutoModelForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True,trust_remote_code=True)
arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=1,dataloader_pin_memory=False,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False)
trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=mdl,train_dataset=trainDS)
trn.train()
trn.save_model(tgt)
tkz.save_pretrained(tgt)