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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.startswith("B-") or not (k.startswith("I-") or k.endswith("|root") or k.find("|l-")>0 or k.find("|r-")>0)]
self.transition=numpy.full((len(x),len(x)),numpy.nan)
for k,v in x.items():
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)
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.nanmax(m[i]+self.transition,axis=1)
k=[numpy.nanargmax(m[0]+self.transition[0])]
for i in range(1,m.shape[0]):
k.append(numpy.nanargmax(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 UniversalDependenciesCausalPipeline(BellmanFordTokenClassificationPipeline):
def __init__(self,**kwargs):
kwargs["aggregation_strategy"]="simple"
super().__init__(**kwargs)
x=self.model.config.label2id
self.root=numpy.full((len(x)),numpy.nan)
self.left_arc=numpy.full((len(x)),numpy.nan)
self.right_arc=numpy.full((len(x)),numpy.nan)
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
if "logits" not in model_outputs:
return self.postprocess(model_outputs[0],**kwargs)
m=model_outputs["logits"][0].numpy()
for i in range(m.shape[0]-1,0,-1):
m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
k=[numpy.nanargmax(m[0]+self.transition[0])]
for i in range(1,m.shape[0]):
k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
w=[{"entity":self.model.config.id2label[j],"start":s,"end":e} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
for i,t in reversed(list(enumerate(w))):
p=t.pop("entity")
if p.startswith("I-"):
w[i-1]["end"]=max(w.pop(i)["end"],w[i-1]["end"])
elif i>0 and w[i-1]["end"]>w[i]["start"]:
w[i-1]["end"]=max(w.pop(i)["end"],w[i-1]["end"])
elif p.startswith("B-"):
t["entity_group"]=p[2:]
else:
t["entity_group"]=p
d=[model_outputs["sentence"][t["start"]:t["end"]] for t in w]
for i in range(len(d)-1,-1,-1):
if d[i].startswith(" "):
j=len(d[i])-len(d[i].lstrip())
d[i]=d[i].lstrip()
w[i]["start"]+=j
if d[i].endswith(" "):
j=len(d[i])-len(d[i].rstrip())
d[i]=d[i].rstrip()
w[i]["end"]-=j
if d[i].strip()=="":
d.pop(i)
w.pop(i)
v=self.tokenizer(d,add_special_tokens=False)
e=self.model.get_input_embeddings().weight
m=[]
for x in v["input_ids"]:
if x==[]:
x=[self.tokenizer.unk_token_id]
m.append(e[x,:].sum(axis=0))
m.append(e[self.tokenizer.sep_token_id,:])
m.append(e[self.tokenizer.pad_token_id,:])
m=torch.stack(m).to(self.device)
k=list(range(len(d)+1))
e=[]
with torch.no_grad():
for i in range(len(d)):
e.append(self.model(inputs_embeds=torch.unsqueeze(m[k+list(range(i,len(d)))+[-1]*i,:],0)).logits[0,-len(d):,:])
e=torch.stack(e).cpu().numpy()
for i in range(len(d)):
for j in range(i):
e[-j-1,-i-1],e[-i-1,-j-1]=e[-i-1,i-j]+self.left_arc,e[-i-1,i-j]+self.right_arc
e[-i-1,-i-1]=e[-i-1,0]+self.root
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)
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,j in enumerate(d):
u+="\t".join([str(i+1),j,"_",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(d) and w[i]["end"]<w[i+1]["start"] 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
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