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from transformers import TokenClassificationPipeline
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
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