import os
from transformers import TokenClassificationPipeline,DebertaV2TokenizerFast
from transformers.models.bert_japanese.tokenization_bert_japanese import MecabTokenizer
try:
  from transformers.utils import cached_file
except:
  from transformers.file_utils import cached_path,hf_bucket_url
  cached_file=lambda x,y:os.path.join(x,y) if os.path.isdir(x) else cached_path(hf_bucket_url(x,y))

class UniversalDependenciesPipeline(TokenClassificationPipeline):
  def _forward(self,model_inputs):
    import torch
    v=model_inputs["input_ids"][0].tolist()
    with torch.no_grad():
      e=self.model(input_ids=torch.tensor([v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)],device=self.device))
    return {"logits":e.logits[:,1:-2,:],**model_inputs}
  def postprocess(self,model_outputs,**kwargs):
    import numpy
    if "logits" not in model_outputs:
      return "".join(self.postprocess(x,**kwargs) for x in model_outputs)
    e=model_outputs["logits"].numpy()
    r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
    e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
    g=self.model.config.label2id["X|_|goeswith"]
    r=numpy.tri(e.shape[0])
    for i in range(e.shape[0]):
      for j in range(i+2,e.shape[1]):
        r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
    e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
    m,p=numpy.nanmax(e,axis=2),numpy.nanargmax(e,axis=2)
    h=self.chu_liu_edmonds(m)
    z=[i for i,j in enumerate(h) if i==j]
    if len(z)>1:
      k,h=z[numpy.nanargmax(m[z,z])],numpy.nanmin(m)-numpy.nanmax(m)
      m[:,z]+=[[0 if j in z and (i!=j or i==k) else h for i in z] for j in range(m.shape[0])]
      h=self.chu_liu_edmonds(m)
    v=[(s,e) for s,e in model_outputs["offset_mapping"][0].tolist() if s<e]
    q=[self.model.config.id2label[p[j,i]].split("|") for i,j in enumerate(h)]
    if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
      for i,j in reversed(list(enumerate(q[1:],1))):
        if j[-1]=="goeswith" and set([t[-1] for t in q[h[i]+1:i+1]])=={"goeswith"}:
          h=[b if i>b else b-1 for a,b in enumerate(h) if i!=a]
          v[i-1]=(v[i-1][0],v.pop(i)[1])
          q.pop(i)
    t=model_outputs["sentence"].replace("\n"," ")
    u="# text = "+t+"\n"
    for i,(s,e) in enumerate(v):
      u+="\t".join([str(i+1),t[s:e],"_",q[i][0],"_","|".join(q[i][1:-1]),str(0 if h[i]==i else h[i]+1),q[i][-1],"_","_" if i+1<len(v) and e<v[i+1][0] else "SpaceAfter=No"])+"\n"
    return u+"\n"
  def chu_liu_edmonds(self,matrix):
    import numpy
    h=numpy.nanargmax(matrix,axis=0)
    x=[-1 if i==j else j for i,j in enumerate(h)]
    for b in [lambda x,i,j:-1 if i not in x else x[i],lambda x,i,j:-1 if j<0 else x[j]]:
      y=[]
      while x!=y:
        y=list(x)
        for i,j in enumerate(x):
          x[i]=b(x,i,j)
      if max(x)<0:
        return h
    y,x=[i for i,j in enumerate(x) if j==max(x)],[i for i,j in enumerate(x) if j<max(x)]
    z=matrix-numpy.nanmax(matrix,axis=0)
    m=numpy.block([[z[x,:][:,x],numpy.nanmax(z[x,:][:,y],axis=1).reshape(len(x),1)],[numpy.nanmax(z[y,:][:,x],axis=0),numpy.nanmax(z[y,y])]])
    k=[j if i==len(x) else x[j] if j<len(x) else y[numpy.nanargmax(z[y,x[i]])] for i,j in enumerate(self.chu_liu_edmonds(m))]
    h=[j if i in y else k[x.index(i)] for i,j in enumerate(h)]
    i=y[numpy.nanargmax(z[x[k[-1]],y] if k[-1]<len(x) else z[y,y])]
    h[i]=x[k[-1]] if k[-1]<len(x) else i
    return h

class MecabPreTokenizer(MecabTokenizer):
  def mecab_split(self,i,normalized_string):
    t=str(normalized_string)
    z=[]
    e=0
    for c in self.tokenize(t):
      s=t.find(c,e)
      e=e if s<0 else s+len(c)
      z.append((0,0) if s<0 else (s,e))
    return [normalized_string[s:e] for s,e in z if e>0]
  def pre_tokenize(self,pretok):
    pretok.split(self.mecab_split)

class JumanDebertaV2TokenizerFast(DebertaV2TokenizerFast):
  def __init__(self,**kwargs):
    from tokenizers.pre_tokenizers import PreTokenizer,Metaspace,Sequence
    super().__init__(**kwargs)
    d,r="/var/lib/mecab/dic/juman-utf8","/etc/mecabrc"
    if not (os.path.isdir(d) and os.path.isfile(r)):
      import zipfile
      import tempfile
      self.dicdir=tempfile.TemporaryDirectory()
      d=self.dicdir.name
      with zipfile.ZipFile(cached_file(self.name_or_path,"mecab-jumandic-utf8.zip")) as z:
        z.extractall(d)
      r=os.path.join(d,"mecabrc")
      with open(r,"w",encoding="utf-8") as w:
        print("dicdir =",d,file=w)
    self.custom_pre_tokenizer=Sequence([PreTokenizer.custom(MecabPreTokenizer(mecab_dic=None,mecab_option="-d "+d+" -r "+r)),Metaspace()])
    self._tokenizer.pre_tokenizer=self.custom_pre_tokenizer
  def save_pretrained(self,save_directory,**kwargs):
    import shutil
    from tokenizers.pre_tokenizers import Metaspace
    self._auto_map={"AutoTokenizer":[None,"ud.JumanDebertaV2TokenizerFast"]}
    self._tokenizer.pre_tokenizer=Metaspace()
    super().save_pretrained(save_directory,**kwargs)
    self._tokenizer.pre_tokenizer=self.custom_pre_tokenizer
    shutil.copy(os.path.abspath(__file__),os.path.join(save_directory,"ud.py"))
    shutil.copy(cached_file(self.name_or_path,"mecab-jumandic-utf8.zip"),os.path.join(save_directory,"mecab-jumandic-utf8.zip"))