import numpy
from transformers import TokenClassificationPipeline

class UniversalDependenciesPipeline(TokenClassificationPipeline):
  def __init__(self,**kwargs):
    super().__init__(**kwargs)
    x=self.model.config.label2id
    self.root=numpy.full((len(x)),numpy.nan)
    self.arc_start=numpy.full((len(x)),numpy.nan)
    self.arc_tail=numpy.full((len(x)),numpy.nan)
    for k,v in x.items():
      if k.endswith("|root"):
        self.root[v]=0
      elif k.endswith("-s"):
        self.arc_start[v]=0
      elif k.endswith("-t"):
        self.arc_tail[v]=0
  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+v[1:]*(len(v)-3)]).to(self.device))
    return {"logits":e.logits,**model_inputs}
  def check_model_type(self,supported_models):
    pass
  def postprocess(self,model_outputs,**kwargs):
    if "logits" not in model_outputs:
      return "".join(self.postprocess(x,**kwargs) for x in model_outputs)
    m=model_outputs["logits"][0].cpu().numpy()
    w=len(model_outputs["input_ids"][0])-2
    e=numpy.zeros((w,w,m.shape[-1]))
    for i in range(w):
      k=numpy.roll(m[i*(w+2)+1]+self.arc_tail,-1)
      for j in range(w):
        if i==j:
          e[i,i]=m[i*(w+1)+j+1]+self.root
        else:
          e[j,i]=m[i*(w+1)+j+1]+self.arc_start+k
    g=self.model.config.label2id["X|goeswith-s"]
    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-s" and set([t[-1] for t in q[h[i]+1:i+1]])=={"goeswith-s"}:
          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)
        elif v[i-1][1]>v[i][0]:
          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],"_","_" if len(q[i])<3 else "|".join(q[i][1:-1]),str(0 if h[i]==i else h[i]+1),"root" if q[i][-1]=="root" else q[i][-1][0:-2],"_","_" if i+1<len(v) and e<v[i+1][0] else "SpaceAfter=No"])+"\n"
    return u+"\n"
  def chu_liu_edmonds(self,matrix):
    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