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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.inf)
self.left_arc=numpy.full((len(x)),-numpy.inf)
self.right_arc=numpy.full((len(x)),-numpy.inf)
for k,v in x.items():
if k.endswith("|root"):
self.root[v]=0
elif k.find("|l-")>0:
self.left_arc[v]=0
elif k.find("|r-")>0:
self.right_arc[v]=0
def check_model_type(self,supported_models):
pass
def postprocess(self,model_outputs,**kwargs):
import torch
if "logits" not in model_outputs:
return "".join(self.postprocess(x,**kwargs) for x in model_outputs)
m=model_outputs["logits"][0].cpu().numpy()
k=numpy.argmax(m,axis=1).tolist()
x=[self.model.config.id2label[i].split("|")[1]=="o" for i in k[1:-1]]
v=model_outputs["input_ids"][0].tolist()
off=model_outputs["offset_mapping"][0].tolist()
for i,(s,e) in reversed(list(enumerate(off))):
if s<e:
d=model_outputs["sentence"][s:e]
j=len(d)-len(d.lstrip())
if j>0:
d=d.lstrip()
off[i][0]+=j
j=len(d)-len(d.rstrip())
if j>0:
d=d.rstrip()
off[i][1]-=j
if d.strip()=="":
off.pop(i)
v.pop(i)
x.pop(i-1)
if len(x)<127:
x=[True]*len(x)
else:
w=sum([len(x)-i+1 if b else 0 for i,b in enumerate(x)])+1
for i in numpy.argsort(numpy.max(m,axis=1)[1:-1]):
if x[i]==False and w+len(x)-i<8192:
x[i]=True
w+=len(x)-i+1
w=[self.tokenizer.cls_token_id]
for i,j in enumerate(x):
if j:
w+=v[i+1:]
with torch.no_grad():
e=self.model(input_ids=torch.tensor([w]).to(self.device))
m=e.logits[0].cpu().numpy()
w=len(v)-2
e=numpy.full((w,w,m.shape[-1]),m.min())
k=1
for i in range(w):
if x[i]:
e[i,i]=m[k]+self.root
k+=1
for j in range(1,w-i):
e[i+j,i]=m[k]+self.left_arc
e[i,i+j]=m[k]+self.right_arc
k+=1
k+=1
g=self.model.config.label2id["X|x|r-goeswith"]
m,r=numpy.max(e,axis=2),numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=1
if numpy.argmax(e[i,j-1])==g and numpy.argmax(m[:,j-1])==i:
r[i,j]=r[i,j-1]
e[:,:,g]+=numpy.where(r==0,0,-numpy.inf)
m,p=numpy.max(e,axis=2),numpy.argmax(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.argmax(m[z,z])],numpy.min(m)-numpy.max(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 off 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]=="r-goeswith" and set([t[-1] for t in q[h[i]+1:i+1]])=={"r-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)
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])<4 else "|".join(q[i][2:-1]),str(0 if h[i]==i else h[i]+1),"root" if q[i][-1]=="root" else q[i][-1][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.argmax(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.max(matrix,axis=0)
m=numpy.block([[z[x,:][:,x],numpy.max(z[x,:][:,y],axis=1).reshape(len(x),1)],[numpy.max(z[y,:][:,x],axis=0),numpy.max(z[y,y])]])
k=[j if i==len(x) else x[j] if j<len(x) else y[numpy.argmax(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.argmax(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
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