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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.arc=numpy.full((len(x)),-numpy.inf)
for k,v in x.items():
if k.endswith("|[root]"):
self.root[v]=0
elif k.endswith("]"):
self.arc[v]=0
def _forward(self,model_inputs):
import torch
v=model_inputs["input_ids"][0].tolist()
if len(v)<91:
x=[True]*(len(v)-2)
else:
with torch.no_grad():
e=self.model(input_ids=torch.tensor([v]).to(self.device))
m=e.logits[0].cpu().numpy()
e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
z=e/e.sum(axis=-1,keepdims=True)
k=numpy.argmax(m,axis=1).tolist()
x=[not self.model.config.id2label[p].split("|")[0].endswith(".") for p in k[1:-1]]
w=(sum([1 for b in x if b])+1)*(len(x)+1)+1
for i in numpy.argsort([z[i+1,k[i+1]] for i in range(len(x))]):
if w+len(x)>8191:
break
if not x[i]:
x[i]=True
w+=len(x)+1
ids=list(v)
for i in range(len(x)):
if x[i]:
ids+=v[1:i+1]+[self.tokenizer.mask_token_id]+v[i+2:]
with torch.no_grad():
e=self.model(input_ids=torch.tensor([ids]).to(self.device))
return {"logits":e.logits,"thin_out":x,**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()
x=model_outputs["thin_out"]
e=numpy.full((len(x),len(x),m.shape[-1]),m.min())
k=len(x)+2
for i in range(len(x)):
if x[i]:
for j in range(len(x)):
if i==j:
e[i,i]=m[k]+self.root
else:
e[i,j]=m[k]+self.arc
k+=1
k+=1
g=self.model.config.label2id["X.|[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:
if 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 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)
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].replace(".",""),"_","_" if len(q[i])<3 else "|".join(q[i][1:-1]),str(0 if h[i]==i else h[i]+1),q[i][-1][1:-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.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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