from transformers import TokenClassificationPipeline

class UniversalDependenciesPipeline(TokenClassificationPipeline):
  def preprocess(self,sentence,offset_mapping=None):
    import torch
    from tokenizers.pre_tokenizers import Whitespace
    v=Whitespace().pre_tokenize_str(sentence)
    t=[v[0]]
    for k,(s,e) in v[1:]:
      j=t[-1][0]+"_"+k
      if self.tokenizer.convert_tokens_to_ids(j)!=self.tokenizer.unk_token_id:
        t[-1]=(j,(t[-1][1][0],e))
      else:
        t.append((k,(s,e)))
    m=[(0,0)]+[j for i,j in t]+[(0,0)]
    r=super().preprocess(sentence=" ".join(i for i,j in t))
    w=self.tokenizer.convert_ids_to_tokens(r["input_ids"][0])
    if len(m)!=len(w):
      for i,j in enumerate(w):
        if j.endswith("@@"):
          s,e=m[i]
          m.insert(i+1,(s+len(j)-2,e))
          m[i]=(s,s+len(j)-2)
    r["offset_mapping"]=torch.tensor([m],device=self.device)
    r["sentence"]=sentence
    return r
  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)]
    g="aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none"
    if g:
      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],t[s:e] if g else "_",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