from transformers import TokenClassificationPipeline

class UniversalDependenciesPipeline(TokenClassificationPipeline):
  def _forward(self,model_input):
    import torch
    v=model_input["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)]))
    return {"logits":e.logits[:,1:-2,:],**model_input}
  def postprocess(self,model_output,**kwargs):
    import numpy
    e=model_output["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=z[numpy.nanargmax(m[z,z])]
      m[:,z]+=[[0 if j in z and (i!=j or i==k) else numpy.nan 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_output["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":
          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_output["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=[i for i,j in enumerate(x) if j==max(x)]
    m=numpy.full((matrix.shape[0]+1,matrix.shape[1]+1),numpy.nan)
    m[0:-1,0:-1]=z=matrix-numpy.nanmax(matrix,axis=0)
    m[0:-1,-1],m[-1,0:-1],m[-1,-1]=numpy.nanmax(z[:,y],axis=1),numpy.nanmax(z[y,:],axis=0),numpy.nanmax(z[y,y])
    m[y,:]=m[:,y]=numpy.nan
    m[y,y]=0
    k=self.chu_liu_edmonds(m)
    j=y[numpy.nanargmax(z[k[-1],y] if k[-1]<z.shape[0] else z[y,y])]
    i=k[-1] if k[-1]<z.shape[0] else j
    z[0:i,j]=z[i+1:,j]=numpy.nan
    return self.chu_liu_edmonds(z)