| import numpy | |
| from transformers import TokenClassificationPipeline,AutoTokenizer | |
| try: | |
| from transformers.utils import cached_file | |
| except: | |
| from transformers.file_utils import cached_path,hf_bucket_url | |
| cached_file=lambda x,y:os.path.join(x,y) if os.path.isdir(x) else cached_path(hf_bucket_url(x,y)) | |
| 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) | |
| self.oldtokenizer=AutoTokenizer.from_pretrained(self.tokenizer.name_or_path,merges_file=cached_file(self.tokenizer.name_or_path,"oldmerges.txt")) | |
| 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.oldtokenizer(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 | |