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import numpy
from transformers import TokenClassificationPipeline
class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
def __init__(self,**kwargs):
super().__init__(**kwargs)
x=self.model.config.label2id
y=[k for k in x if k.find("|")<0 and not k.startswith("I-")]
self.transition=numpy.full((len(x),len(x)),-numpy.inf)
for k,v in x.items():
if k.find("|")<0:
for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
self.transition[v,x[j]]=0
def check_model_type(self,supported_models):
pass
def postprocess(self,model_outputs,**kwargs):
if "logits" not in model_outputs:
return self.postprocess(model_outputs[0],**kwargs)
return self.bellman_ford_token_classification(model_outputs,**kwargs)
def bellman_ford_token_classification(self,model_outputs,**kwargs):
m=model_outputs["logits"][0].numpy()
e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
z=e/e.sum(axis=-1,keepdims=True)
for i in range(m.shape[0]-1,0,-1):
m[i-1]+=numpy.max(m[i]+self.transition,axis=1)
k=[numpy.argmax(m[0]+self.transition[0])]
for i in range(1,m.shape[0]):
k.append(numpy.argmax(m[i]+self.transition[k[-1]]))
w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
for i,t in reversed(list(enumerate(w))):
p=t.pop("entity")
if p.startswith("I-"):
w[i-1]["score"]=min(w[i-1]["score"],t["score"])
w[i-1]["end"]=w.pop(i)["end"]
elif p.startswith("B-"):
t["entity_group"]=p[2:]
else:
t["entity_group"]=p
for t in w:
t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
return w
class UniversalDependenciesPipeline(BellmanFordTokenClassificationPipeline):
def __init__(self,**kwargs):
kwargs["aggregation_strategy"]="simple"
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 postprocess(self,model_outputs,**kwargs):
import torch
kwargs["aggregation_strategy"]="simple"
if "logits" not in model_outputs:
return self.postprocess(model_outputs[0],**kwargs)
w=self.bellman_ford_token_classification(model_outputs,**kwargs)
off=[(t["start"],t["end"]) for t in w]
for i,(s,e) in reversed(list(enumerate(off))):
if s<e:
d=w[i]["text"]
j=len(d)-len(d.lstrip())
if j>0:
d=d.lstrip()
off[i]=(off[i][0]+j,off[i][1])
j=len(d)-len(d.rstrip())
if j>0:
d=d.rstrip()
off[i]=(off[i][0],off[i][1]-j)
if d.strip()=="":
off.pop(i)
w.pop(i)
v=self.tokenizer([t["text"] for t in w],add_special_tokens=False)
x=[not t["entity_group"].endswith(".") for t in w]
if len(x)<127:
x=[True]*len(x)
else:
k=sum([len(x)-i+1 if b else 0 for i,b in enumerate(x)])+1
for i in numpy.argsort(numpy.array([t["score"] for t in w])):
if x[i]==False and k+len(x)-i<8192:
x[i]=True
k+=len(x)-i+1
ids=[-1]
for i in range(len(x)):
if x[i]:
ids.append(i)
for j in range(i+1,len(x)):
ids.append(j)
ids.append(-1)
with torch.no_grad():
e=self.model.get_input_embeddings().weight
m=[]
for j in v["input_ids"]:
if j==[]:
j=[self.tokenizer.unk_token_id]
m.append(e[j,:].sum(axis=0))
m.append(e[self.tokenizer.sep_token_id,:])
m=torch.stack(m).to(self.device)
e=self.model(inputs_embeds=torch.unsqueeze(m[ids,:],0))
m=e.logits[0].cpu().numpy()
e=numpy.full((len(x),len(x),m.shape[-1]),m.min())
k=1
for i in range(len(x)):
if x[i]:
e[i,i]=m[k]+self.root
k+=1
for j in range(1,len(x)-i):
e[i+j,i]=m[k]+self.left_arc
e[i,i+j]=m[k]+self.right_arc
k+=1
k+=1
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)
q=[self.model.config.id2label[p[j,i]].split("|") for i,j in enumerate(h)]
t=model_outputs["sentence"].replace("\n"," ")
u="# text = "+t+"\n"
for i,(s,e) in enumerate(off):
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][2:],"_","_" if i+1<len(off) and e<off[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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