BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/interface/predictor.py

'''
Created on 2018年12月26日

@author: User
'''

import os
import sys
sys.path.append(os.path.abspath("../.."))
# from keras.engine import topology
# from keras import models
# from keras import layers
# from keras_contrib.layers.crf import CRF
# from keras.preprocessing.sequence import pad_sequences
# from keras import optimizers,losses,metrics
from BiddingKG.dl.common.Utils import *
from BiddingKG.dl.interface.modelFactory import *
import tensorflow as tf
from tensorflow.python.framework import graph_util
from BiddingKG.dl.product.data_util import decode, process_data, result_to_json
from BiddingKG.dl.interface.Entitys import Entity

from threading import RLock
dict_predictor = {"codeName":{"predictor":None,"Lock":RLock()},
              "prem":{"predictor":None,"Lock":RLock()},
              "epc":{"predictor":None,"Lock":RLock()},
              "roleRule":{"predictor":None,"Lock":RLock()},
                  "form":{"predictor":None,"Lock":RLock()}}


def getPredictor(_type):
    if _type in dict_predictor:
        with dict_predictor[_type]["Lock"]:
            if dict_predictor[_type]["predictor"] is None:
                if _type=="codeName":
                    dict_predictor[_type]["predictor"] = CodeNamePredict()
                if _type=="prem":
                    dict_predictor[_type]["predictor"] = PREMPredict()
                if _type=="epc":
                    dict_predictor[_type]["predictor"] = EPCPredict()
                if _type=="roleRule":
                    dict_predictor[_type]["predictor"] = RoleRulePredictor()
                if _type=="form":
                    dict_predictor[_type]["predictor"] = FormPredictor()
            return dict_predictor[_type]["predictor"]
    raise NameError("no this type of predictor")

#编号名称模型
class CodeNamePredict():
    
    def __init__(self,EMBED_DIM=None,BiRNN_UNITS=None,lazyLoad=getLazyLoad()):
        
        self.model = None
        self.MAX_LEN = None
        self.model_code = None
        if EMBED_DIM is None:
            self.EMBED_DIM = 60
        else:
            self.EMBED_DIM = EMBED_DIM
        if BiRNN_UNITS is None:
            self.BiRNN_UNITS = 200
        else:
            self.BiRNN_UNITS = BiRNN_UNITS
        self.filepath = os.path.dirname(__file__)+"/../projectCode/models/model_project_"+str(self.EMBED_DIM)+"_"+str(self.BiRNN_UNITS)+".hdf5"
        #self.filepath = "../projectCode/models/model_project_60_200_200ep017-loss6.456-val_loss7.852-val_acc0.969.hdf5"
        self.filepath_code = os.path.dirname(__file__)+"/../projectCode/models/model_code.hdf5"
        vocabpath = os.path.dirname(__file__)+"/codename_vocab.pk"
        classlabelspath = os.path.dirname(__file__)+"/codename_classlabels.pk"
        self.vocab = load(vocabpath)
        self.class_labels = load(classlabelspath)
        
        #生成提取编号和名称的正则
        id_PC_B = self.class_labels.index("PC_B")
        id_PC_M = self.class_labels.index("PC_M")
        id_PC_E = self.class_labels.index("PC_E")
        id_PN_B = self.class_labels.index("PN_B")
        id_PN_M = self.class_labels.index("PN_M")
        id_PN_E = self.class_labels.index("PN_E")
        self.PC_pattern = re.compile(str(id_PC_B)+str(id_PC_M)+"*"+str(id_PC_E))
        self.PN_pattern = re.compile(str(id_PN_B)+str(id_PN_M)+"*"+str(id_PN_E))
        print("pc",self.PC_pattern)
        print("pn",self.PN_pattern)
        self.word2index = dict((w,i) for i,w in enumerate(np.array(self.vocab)))
        
        self.inputs = None
        self.outputs = None
        self.sess_codename = tf.Session(graph=tf.Graph())
        self.sess_codesplit = tf.Session(graph=tf.Graph())
        self.inputs_code = None
        self.outputs_code = None
        if not lazyLoad:
            self.getModel()
            self.getModel_code()
        
        
        
    def getModel(self):
        '''
        @summary: 取得编号和名称模型
        '''
        if self.inputs is None:
            log("get model of codename")
            with self.sess_codename.as_default():
                with self.sess_codename.graph.as_default():
                    meta_graph_def = tf.saved_model.loader.load(self.sess_codename, ["serve"], export_dir=os.path.dirname(__file__)+"/codename_savedmodel_tf")
                    signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                    signature_def = meta_graph_def.signature_def
                    self.inputs = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].inputs["inputs"].name)
                    self.inputs_length = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].inputs["inputs_length"].name)
                    self.keepprob = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].inputs["keepprob"].name)
                    self.logits = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].outputs["logits"].name)
                    self.trans = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].outputs["trans"].name)

                return self.inputs,self.inputs_length,self.keepprob,self.logits,self.trans
        else:
            return self.inputs,self.inputs_length,self.keepprob,self.logits,self.trans
        '''    
        if self.model is None:
            self.model = self.getBiLSTMCRFModel(self.MAX_LEN, self.vocab, self.EMBED_DIM, self.BiRNN_UNITS, self.class_labels,weights=None)
            self.model.load_weights(self.filepath)
        return self.model
        '''
    
    def getModel_code(self):
        if self.inputs_code is None:
            log("get model of code")
            with self.sess_codesplit.as_default():
                with self.sess_codesplit.graph.as_default():
                    meta_graph_def = tf.saved_model.loader.load(self.sess_codesplit, ["serve"], export_dir=os.path.dirname(__file__)+"/codesplit_savedmodel")
                    signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                    signature_def = meta_graph_def.signature_def
                    self.inputs_code = []
                    self.inputs_code.append(self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].inputs["input0"].name))
                    self.inputs_code.append(self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].inputs["input1"].name))
                    self.inputs_code.append(self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].inputs["input2"].name))
                    self.outputs_code = self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].outputs["outputs"].name)
                    self.sess_codesplit.graph.finalize()
                    return self.inputs_code,self.outputs_code
        else:
            return self.inputs_code,self.outputs_code
        '''
        if self.model_code is None:
            log("get model of model_code")
            with self.sess_codesplit.as_default():
                with self.sess_codesplit.graph.as_default():
                    self.model_code = models.load_model(self.filepath_code, custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
        return self.model_code
        '''
    
    def getBiLSTMCRFModel(self,MAX_LEN,vocab,EMBED_DIM,BiRNN_UNITS,chunk_tags,weights):
        '''
        model = models.Sequential()
        model.add(layers.Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
        model.add(layers.Bidirectional(layers.LSTM(BiRNN_UNITS // 2, return_sequences=True)))
        crf = CRF(len(chunk_tags), sparse_target=True)
        model.add(crf)
        model.summary()
        model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
        return model
        '''
        input = layers.Input(shape=(None,))
        if weights is not None:
            embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True,weights=[weights],trainable=True)(input)
        else:
            embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True)(input)
        bilstm = layers.Bidirectional(layers.LSTM(BiRNN_UNITS//2,return_sequences=True))(embedding)
        bilstm_dense = layers.TimeDistributed(layers.Dense(len(chunk_tags)))(bilstm)
        crf = CRF(len(chunk_tags),sparse_target=True)
        crf_out = crf(bilstm_dense)
        model = models.Model(input=[input],output = [crf_out])
        model.summary()
        model.compile(optimizer = 'adam', loss = crf.loss_function, metrics = [crf.accuracy])
        return model
    
    #根据规则补全编号或名称两边的符号
    def fitDataByRule(self,data):
        symbol_dict = {"(":")",
                       "（":"）",
                       "[":"]",
                       "【":"】",
                       ")":"(",
                       "）":"（",
                       "]":"[",
                       "】":"【"}
        leftSymbol_pattern = re.compile("[\(（\[【]")
        rightSymbol_pattern = re.compile("[\)）\]】]")
        leftfinds = re.findall(leftSymbol_pattern,data)
        rightfinds = re.findall(rightSymbol_pattern,data)
        result = data
        if len(leftfinds)+len(rightfinds)==0:
            return data
        elif len(leftfinds)==len(rightfinds):
            return data
        elif abs(len(leftfinds)-len(rightfinds))==1:
            if len(leftfinds)>len(rightfinds):
                if symbol_dict.get(data[0]) is not None:
                    result = data[1:]
                else:
                    #print(symbol_dict.get(leftfinds[0]))
                    result = data+symbol_dict.get(leftfinds[0])
            else:
                if symbol_dict.get(data[-1]) is not None:
                    result = data[:-1]
                else:
                    result = symbol_dict.get(rightfinds[0])+data
        return  result

    def decode(self,logits, trans, sequence_lengths, tag_num):
        viterbi_sequences = []
        for logit, length in zip(logits, sequence_lengths):
            score = logit[:length]
            viterbi_seq, viterbi_score = viterbi_decode(score, trans)
            viterbi_sequences.append(viterbi_seq)
        return viterbi_sequences
    
    def predict(self,list_sentences,list_entitys=None,MAX_AREA = 5000):
        #@summary: 获取每篇文章的code和name
        
        pattern_score = re.compile("工程|服务|采购|施工|项目|系统|招标|中标|公告|学校|[大中小]学校?|医院|公司|分公司|研究院|政府采购中心|学院|中心校?|办公室|政府|财[政务]局|办事处|委员会|[部总支]队|警卫局|幼儿园|党委|党校|银行|分行|解放军|发电厂|供电局|管理所|供电公司|卷烟厂|机务段|研究[院所]|油厂|调查局|调查中心|出版社|电视台|监狱|水厂|服务站|信用合作联社|信用社|交易所|交易中心|交易中心党校|科学院|测绘所|运输厅|管理处|局|中心|机关|部门?|处|科|厂|集团|图书馆|馆|所|厅|楼|区|酒店|场|基地|矿|餐厅|酒店")

        result = []
        index_unk = self.word2index.get("<unk>")
        # index_pad = self.word2index.get("<pad>")
        if list_entitys is None:
            list_entitys = [[] for _ in range(len(list_sentences))]
        for list_sentence,list_entity in zip(list_sentences,list_entitys):
            if len(list_sentence)==0:
                result.append([{"code":[],"name":""}])
                continue
            doc_id = list_sentence[0].doc_id
            # sentences = []
            # for sentence in list_sentence:
            #     if len(sentence.sentence_text)>MAX_AREA:
            #         for _sentence_comma in re.split("[;；，\n]",sentence):
            #             _comma_index = 0
            #             while(_comma_index<len(_sentence_comma)):
            #                 sentences.append(_sentence_comma[_comma_index:_comma_index+MAX_AREA])
            #                 _comma_index += MAX_AREA
            #     else:
            #         sentences.append(sentence+"。")
            list_sentence.sort(key=lambda x:len(x.sentence_text),reverse=True)
            _begin_index = 0
            
            item = {"code":[],"name":""}
            code_set = set()
            dict_name_freq_score = dict()
            while(True):
                MAX_LEN = len(list_sentence[_begin_index].sentence_text)
                if MAX_LEN>MAX_AREA:
                    MAX_LEN = MAX_AREA
                _LEN = MAX_AREA//MAX_LEN
                #预测

                x = [[self.word2index.get(word,index_unk)for word in sentence.sentence_text[:MAX_AREA]]for sentence in list_sentence[_begin_index:_begin_index+_LEN]]
                # x = [[getIndexOfWord(word) for word in sentence.sentence_text[:MAX_AREA]]for sentence in list_sentence[_begin_index:_begin_index+_LEN]]
                x_len = [len(_x) if len(_x) < MAX_LEN else MAX_LEN for _x in x]
                x = pad_sequences(x,maxlen=MAX_LEN,padding="post",truncating="post")

                if USE_PAI_EAS:
                    request = tf_predict_pb2.PredictRequest()
                    request.inputs["inputs"].dtype = tf_predict_pb2.DT_INT32
                    request.inputs["inputs"].array_shape.dim.extend(np.shape(x))
                    request.inputs["inputs"].int_val.extend(np.array(x,dtype=np.int32).reshape(-1))
                    request_data = request.SerializeToString()
                    list_outputs = ["outputs"]
                    _result = vpc_requests(codename_url, codename_authorization, request_data, list_outputs)
                    if _result is not None:
                        predict_y = _result["outputs"]
                    else:
                        with self.sess_codename.as_default():
                            t_input,t_output = self.getModel()
                            predict_y = self.sess_codename.run(t_output,feed_dict={t_input:x})
                else:
                    with self.sess_codename.as_default():
                        t_input,t_input_length,t_keepprob,t_logits,t_trans = self.getModel()
                        _logits,_trans = self.sess_codename.run([t_logits,t_trans],feed_dict={t_input:x,
                                                                                              t_input_length:x_len,
                                                                                              t_keepprob:1.0})
                        predict_y = self.decode(_logits,_trans,x_len,7)
                        # print('==========',_logits)

                        '''
                        for item11 in np.argmax(predict_y,-1):
                            print(item11)
                        print(predict_y)
                        '''
                # print(predict_y)
                for sentence,predict in zip(list_sentence[_begin_index:_begin_index+_LEN],np.array(predict_y)):
                    pad_sentence = sentence.sentence_text[:MAX_LEN]
                    join_predict = "".join([str(s) for s in predict])
                    # print(pad_sentence)
                    # print(join_predict)
                    code_x = []
                    code_text = []
                    temp_entitys = []
                    for iter in re.finditer(self.PC_pattern,join_predict):
                        get_len = 40
                        if iter.span()[0]<get_len:
                            begin = 0
                        else:
                            begin = iter.span()[0]-get_len
                        end = iter.span()[1]+get_len
                        code_x.append(embedding_word([pad_sentence[begin:iter.span()[0]],pad_sentence[iter.span()[0]:iter.span()[1]],pad_sentence[iter.span()[1]:end]],shape=(3,get_len,60)))
                        code_text.append(pad_sentence[iter.span()[0]:iter.span()[1]])
                        _entity = Entity(doc_id=sentence.doc_id,entity_id="%s_%s_%s_%s"%(sentence.doc_id,sentence.sentence_index,iter.span()[0],iter.span()[1]),entity_text=pad_sentence[iter.span()[0]:iter.span()[1]],entity_type="code",sentence_index=sentence.sentence_index,begin_index=0,end_index=0,wordOffset_begin=iter.span()[0],wordOffset_end=iter.span()[1])
                        temp_entitys.append(_entity)
                    #print("code",code_text)
                    if len(code_x)>0:
                        code_x = np.transpose(np.array(code_x,dtype=np.float32),(1,0,2,3))
                        if USE_PAI_EAS:
                            request = tf_predict_pb2.PredictRequest()
                            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
                            request.inputs["input0"].array_shape.dim.extend(np.shape(code_x[0]))
                            request.inputs["input0"].float_val.extend(np.array(code_x[0],dtype=np.float64).reshape(-1))
                            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
                            request.inputs["input1"].array_shape.dim.extend(np.shape(code_x[1]))
                            request.inputs["input1"].float_val.extend(np.array(code_x[1],dtype=np.float64).reshape(-1))
                            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
                            request.inputs["input2"].array_shape.dim.extend(np.shape(code_x[2]))
                            request.inputs["input2"].float_val.extend(np.array(code_x[2],dtype=np.float64).reshape(-1))
                            request_data = request.SerializeToString()
                            list_outputs = ["outputs"]
                            _result = vpc_requests(codeclasses_url, codeclasses_authorization, request_data, list_outputs)
                            if _result is not None:
                                predict_code = _result["outputs"]
                            else:
                                with self.sess_codesplit.as_default():
                                    with self.sess_codesplit.graph.as_default():
                                        predict_code = self.getModel_code().predict([code_x[0],code_x[1],code_x[2]])
                        else:
                            with self.sess_codesplit.as_default():
                                with self.sess_codesplit.graph.as_default():
                                    inputs_code,outputs_code = self.getModel_code()
                                    predict_code = limitRun(self.sess_codesplit,[outputs_code],feed_dict={inputs_code[0]:code_x[0],inputs_code[1]:code_x[1],inputs_code[2]:code_x[2]},MAX_BATCH=2)[0]

                                    #predict_code = self.sess_codesplit.run(outputs_code,feed_dict={inputs_code[0]:code_x[0],inputs_code[1]:code_x[1],inputs_code[2]:code_x[2]})
                                    #predict_code = self.getModel_code().predict([code_x[0],code_x[1],code_x[2]])
                        for h in range(len(predict_code)):
                            if predict_code[h][0]>0.5:
                                the_code = self.fitDataByRule(code_text[h])

                                #add code to entitys
                                list_entity.append(temp_entitys[h])

                                if the_code not in code_set:
                                    code_set.add(the_code)
                                    item['code'] = list(code_set)
                    for iter in re.finditer(self.PN_pattern,join_predict):
                        _name = self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])

                        #add name to entitys
                        _entity = Entity(doc_id=sentence.doc_id,entity_id="%s_%s_%s_%s"%(sentence.doc_id,sentence.sentence_index,iter.span()[0],iter.span()[1]),entity_text=_name,entity_type="name",sentence_index=sentence.sentence_index,begin_index=0,end_index=0,wordOffset_begin=iter.span()[0],wordOffset_end=iter.span()[1])
                        list_entity.append(_entity)
                        w = 1 if re.search('(项目|工程|招标|合同|标项|标的|计划|询价|询价单|询价通知书|申购)(名称|标题|主题)[:：\s]', pad_sentence[iter.span()[0]-10:iter.span()[0]])!=None else 0.5
                        if _name not in dict_name_freq_score:
                            # dict_name_freq_score[_name] = [1,len(re.findall(pattern_score,_name))+len(_name)*0.1]
                            dict_name_freq_score[_name] = [1, (len(re.findall(pattern_score, _name)) + len(_name) * 0.05)*w]
                        else:
                            dict_name_freq_score[_name][0] += 1
                    '''
                    for iter in re.finditer(self.PN_pattern,join_predict):
                        print("name-",self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]]))
                    if item[1]['name']=="":
                        for iter in re.finditer(self.PN_pattern,join_predict):
                            #item[1]['name']=item[1]['name']+";"+self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                            item[1]['name']=self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                            break
                    '''
                if _begin_index+_LEN>=len(list_sentence):
                    break
                _begin_index += _LEN
            
            list_name_freq_score = []

            # 2020/11/23 大网站规则调整
            if len(dict_name_freq_score) == 0:
                name_re1 = '(项目|工程|招标|合同|标项|标的|计划|询价|询价单|询价通知书|申购)(名称|标题|主题)[:：\s]+([^，。：；]{2,60})[，。]'
                for sentence in list_sentence:
                    # pad_sentence = sentence.sentence_text
                    othername = re.search(name_re1, sentence.sentence_text)
                    if othername != None:
                        project_name = othername.group(3)
                        beg = find_index([project_name], sentence.sentence_text)[0]
                        end = beg + len(project_name)
                        _name = self.fitDataByRule(sentence.sentence_text[beg:end])
                        # add name to entitys
                        _entity = Entity(doc_id=sentence.doc_id, entity_id="%s_%s_%s_%s" % (
                        sentence.doc_id, sentence.sentence_index, beg, end), entity_text=_name,
                                         entity_type="name", sentence_index=sentence.sentence_index, begin_index=0,
                                         end_index=0, wordOffset_begin=beg, wordOffset_end=end)
                        list_entity.append(_entity)
                        w = 1
                        if _name not in dict_name_freq_score:
                            # dict_name_freq_score[_name] = [1,len(re.findall(pattern_score,_name))+len(_name)*0.1]
                            dict_name_freq_score[_name] = [1, (len(re.findall(pattern_score, _name)) + len(_name) * 0.05) * w]
                        else:
                            dict_name_freq_score[_name][0] += 1
                # othername = re.search(name_re1, sentence.sentence_text)
                # if othername != None:
                #     _name = othername.group(3)
                #     if _name not in dict_name_freq_score:
                #         dict_name_freq_score[_name] = [1, len(re.findall(pattern_score, _name)) + len(_name) * 0.1]
                #     else:
                #         dict_name_freq_score[_name][0] += 1

            for _name in dict_name_freq_score.keys():
                list_name_freq_score.append([_name,dict_name_freq_score[_name]])
            # print(list_name_freq_score)
            if len(list_name_freq_score)>0:
                list_name_freq_score.sort(key=lambda x:x[1][0]*x[1][1],reverse=True)
                item['name'] = list_name_freq_score[0][0]
                # if list_name_freq_score[0][1][0]>1:
                #     item[1]['name'] = list_name_freq_score[0][0]
                # else:
                #     list_name_freq_score.sort(key=lambda x:x[1][1],reverse=True)
                #     item[1]["name"] = list_name_freq_score[0][0]
                
            #下面代码加上去用正则添加某些识别不到的项目编号
            if item['code'] == []:
                for sentence in list_sentence:
                    # othercode = re.search('(采购计划编号|询价编号)[\)）]?[:：]?([\[\]a-zA-Z0-9\-]{5,30})', sentence.sentence_text)
                    # if othercode != None:
                    #     item[1]['code'].append(othercode.group(2))
                    # 2020/11/23 大网站规则调整
                    othercode = re.search('(项目|采购|招标|品目|询价|竞价|询价单|磋商|订单|账单|交易|文件|计划|场次|标的|标段|标包|分包|标段\(包\)|招标文件|合同|通知书|公告)(单号|编号|标号|编码|代码|备案号|号)[:：\s]+([^，。；：、]{8,30}[a-zA-Z0-9\号])[\)，。]', sentence.sentence_text)
                    if othercode != None:
                        item['code'].append(othercode.group(3))
            result.append(item)

            list_sentence.sort(key=lambda x: x.sentence_index,reverse=False)
        return result
                        
                
    '''
    #当数据量过大时会报错
    def predict(self,articles,MAX_LEN = None):
        sentences = []
        for article in articles:
            for sentence in article.content.split("。"):
                sentences.append([sentence,article.id])
        if MAX_LEN is None:
            sent_len = [len(sentence[0]) for sentence in sentences]
            MAX_LEN = max(sent_len)
            #print(MAX_LEN)
           
        #若为空，则直接返回空
        result = [] 
        if MAX_LEN==0:
            for article in articles:
                result.append([article.id,{"code":[],"name":""}])
            return result
        
        index_unk = self.word2index.get("<unk>")
        index_pad = self.word2index.get("<pad>")
        
        x = [[self.word2index.get(word,index_unk)for word in sentence[0]]for sentence in sentences]
        x = pad_sequences(x,maxlen=MAX_LEN,padding="post",truncating="post")
        
        predict_y = self.getModel().predict(x)
        
        
        last_doc_id = ""
        item = []
        for sentence,predict in zip(sentences,np.argmax(predict_y,-1)):
            pad_sentence = sentence[0][:MAX_LEN]
            doc_id = sentence[1]
            join_predict = "".join([str(s) for s in predict])
            if doc_id!=last_doc_id:
                if last_doc_id!="":
                    result.append(item)
                item = [doc_id,{"code":[],"name":""}]
                code_set = set()
            code_x = []
            code_text = []
            for iter in re.finditer(self.PC_pattern,join_predict):
                get_len = 40
                if iter.span()[0]<get_len:
                    begin = 0
                else:
                    begin = iter.span()[0]-get_len
                end = iter.span()[1]+get_len
                code_x.append(embedding_word([pad_sentence[begin:iter.span()[0]],pad_sentence[iter.span()[0]:iter.span()[1]],pad_sentence[iter.span()[1]:end]],shape=(3,get_len,60)))
                code_text.append(pad_sentence[iter.span()[0]:iter.span()[1]])
            if len(code_x)>0:
                code_x = np.transpose(np.array(code_x),(1,0,2,3))
                predict_code = self.getModel_code().predict([code_x[0],code_x[1],code_x[2]])
                for h in range(len(predict_code)):
                    if predict_code[h][0]>0.5:
                        the_code = self.fitDataByRule(code_text[h])
                        if the_code not in code_set:
                            code_set.add(the_code)
                            item[1]['code'] = list(code_set)
            if item[1]['name']=="":
                for iter in re.finditer(self.PN_pattern,join_predict):
                    #item[1]['name']=item[1]['name']+";"+self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                    item[1]['name']=self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                    break
                
            last_doc_id = doc_id
        result.append(item)
        return result
    '''
        
#角色金额模型        
class PREMPredict():

    
    def __init__(self):
        #self.model_role_file = os.path.abspath("../role/models/model_role.model.hdf5")
        self.model_role_file = os.path.dirname(__file__)+"/../role/log/new_biLSTM-ep012-loss0.028-val_loss0.040-f10.954.h5"
        self.model_role = Model_role_classify_word()
        self.model_money = Model_money_classify()
        
        return
    
    def search_role_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询角色模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:角色模型的输入数据
        '''
        data_x = []
        points_entitys = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):
            
            p_entitys = 0
            p_sentences = 0
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type in ['org','company']:
                    while(p_sentences<len(list_sentence)):
                        sentence = list_sentence[p_sentences]
                        if entity.doc_id==sentence.doc_id and entity.sentence_index==sentence.sentence_index:
                            #item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_ROLE_INPUT_SHAPE[1]),shape=settings.MODEL_ROLE_INPUT_SHAPE)
                            item_x = self.model_role.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,entity_text=entity.entity_text)
                            data_x.append(item_x)
                            points_entitys.append(entity)
                            break
                        p_sentences += 1
                    
                p_entitys += 1
            
        if len(points_entitys)==0:
            return None
        
        return [data_x,points_entitys]
    
    
    def search_money_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询金额模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:金额模型的输入数据
        '''
        data_x = []
        points_entitys = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):
            
            p_entitys = 0
    
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type=="money":
                    p_sentences = 0
                    while(p_sentences<len(list_sentence)):
                        sentence = list_sentence[p_sentences]
                        if entity.doc_id==sentence.doc_id and entity.sentence_index==sentence.sentence_index:
                            #item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_MONEY_INPUT_SHAPE[1]),shape=settings.MODEL_MONEY_INPUT_SHAPE)
                            #item_x = embedding_word(spanWindow(tokens=sentence.tokens, begin_index=entity.begin_index, end_index=entity.end_index, size=10, center_include=True, word_flag=True),shape=settings.MODEL_MONEY_INPUT_SHAPE)
                            item_x = self.model_money.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index)
                            data_x.append(item_x)
                            points_entitys.append(entity)
                            break
                        p_sentences += 1
                p_entitys += 1
        
        if len(points_entitys)==0:
            return None
        
        return [data_x,points_entitys]
    
    def predict_role(self,list_sentences, list_entitys):
        datas = self.search_role_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        
        if USE_PAI_EAS:
            _data = datas[0]
            _data = np.transpose(np.array(_data),(1,0,2))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input2"].array_shape.dim.extend(np.shape(_data[2]))
            request.inputs["input2"].float_val.extend(np.array(_data[2],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(role_url, role_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_role.predict(datas[0])
        else:
            predict_y = self.model_role.predict(np.array(datas[0],dtype=np.float64))
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = []
            for item in predict_y[i]:
                values.append(item)
                entity.set_Role(label,values)
        
    def predict_money(self,list_sentences,list_entitys):
        datas = self.search_money_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        _data = datas[0]
        if USE_PAI_EAS:
            _data = np.transpose(np.array(_data),(1,0,2,3))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input2"].array_shape.dim.extend(np.shape(_data[2]))
            request.inputs["input2"].float_val.extend(np.array(_data[2],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(money_url, money_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_money.predict(_data)
        else:
            predict_y = self.model_money.predict(_data)
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = []
            for item in predict_y[i]:
                values.append(item)
                entity.set_Money(label,values)
        
    def predict(self,list_sentences,list_entitys):
        self.predict_role(list_sentences,list_entitys)
        self.predict_money(list_sentences,list_entitys)
        
        
#联系人模型    
class EPCPredict():
    
    def __init__(self):
        self.model_person = Model_person_classify()


    
    def search_person_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询联系人模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:联系人模型的输入数据
        '''
        def phoneFromList(phones):
            for phone in phones:
                if len(phone)==11:
                    return re.sub('电话[：|:]|联系方式[：|:]','',phone)
            return phones[0]
        data_x = []
        dianhua = []
        points_entitys = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):
            
            p_entitys = 0
            p_sentences = 0

            key_word = re.compile('电话[：|:]\d{7,12}|联系方式[：|:]\d{7,12}')
            # phone = re.compile('1[3|4|5|7|8][0-9][-—－]?\d{4}[-—－]?\d{4}|\d{3,4}[-—－]\d{7,8}/\d{3,8}|\d{3,4}[-—－]\d{7,8}转\d{1,4}|\d{3,4}[-—－]\d{7,8}|[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}')  # 联系电话
            # 2020/11/25 增加发现的号码段
            phone = re.compile('1[3|4|5|6|7|8|9][0-9][-—－]?\d{4}[-—－]?\d{4}|\d{3,4}[-—－]\d{7,8}/\d{3,8}|\d{3,4}[-—－]\d{7,8}转\d{1,4}|\d{3,4}[-—－]\d{7,8}|[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}')  # 联系电话

            dict_index_sentence = {}
            for _sentence in list_sentence:
                dict_index_sentence[_sentence.sentence_index] = _sentence

            dict_context_itemx = {}
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type=="person":
                    sentence = dict_index_sentence[entity.sentence_index]
                    #item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_PERSON_INPUT_SHAPE[1]),shape=settings.MODEL_PERSON_INPUT_SHAPE)

                    s = spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=20)
                    _key = "".join(["".join(x) for x in s])
                    if _key in dict_context_itemx:
                        item_x = dict_context_itemx[_key][0]
                        _dianhua = dict_context_itemx[_key][1]
                    else:
                        item_x = self.model_person.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index)
                        s1 = ''.join(s[1])
                        #s1 = re.sub('，）', '-', s1)
                        s1 = re.sub('\s','',s1)
                        have_key = re.findall(key_word, s1)
                        have_phone = re.findall(phone, s1)
                        s0 = ''.join(s[0])
                        #s0 = re.sub('，）', '-', s0)
                        s0 = re.sub('\s','',s0)
                        have_key2 = re.findall(key_word, s0)
                        have_phone2 = re.findall(phone, s0)

                        s3 = ''.join(s[1])
                        #s0 = re.sub('，）', '-', s0)
                        s3 = re.sub(',|，|\s','',s3)
                        have_key3 = re.findall(key_word, s3)
                        have_phone3 = re.findall(phone, s3)

                        s4 = ''.join(s[0])
                        #s0 = re.sub('，）', '-', s0)
                        s4 = re.sub(',|，|\s','',s0)
                        have_key4 = re.findall(key_word, s4)
                        have_phone4 = re.findall(phone, s4)


                        _dianhua = ""
                        if have_phone:
                            _dianhua = phoneFromList(have_phone)
                        elif have_key:
                            _dianhua = phoneFromList(have_key)
                        elif have_phone2:
                            _dianhua = phoneFromList(have_phone2)
                        elif have_key2:
                            _dianhua =phoneFromList(have_key2)
                        elif have_phone3:
                            _dianhua = phoneFromList(have_phone3)
                        elif have_key3:
                            _dianhua = phoneFromList(have_key3)
                        elif have_phone4:
                            _dianhua = phoneFromList(have_phone4)
                        elif have_key4:
                            _dianhua = phoneFromList(have_key4)
                        else:
                            _dianhua = ""
                        dict_context_itemx[_key] = [item_x,_dianhua]
                    data_x.append(item_x)
                    points_entitys.append(entity)
                    dianhua.append(_dianhua)
                p_entitys += 1


        if len(points_entitys)==0:
            return None
        
        return [data_x,points_entitys,dianhua]
    
    def predict_person(self,list_sentences, list_entitys):
        datas = self.search_person_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        phone = datas[2]
        if USE_PAI_EAS:
            _data = datas[0]
            _data = np.transpose(np.array(_data),(1,0,2,3))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(person_url, person_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_person.predict(datas[0])
        else:
            predict_y = self.model_person.predict(datas[0])
        assert len(predict_y)==len(points_entitys)==len(phone)
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = []
            for item in predict_y[i]:
                values.append(item)
            phone_number = phone[i]
            entity.set_Person(label,values,phone_number)
        
    def predict(self,list_sentences,list_entitys):
        self.predict_person(list_sentences,list_entitys)
            
#表格预测
class FormPredictor():
    
    def __init__(self,lazyLoad=getLazyLoad()):
        self.model_file_line = os.path.dirname(__file__)+"/../form/model/model_form.model_line.hdf5"
        self.model_file_item = os.path.dirname(__file__)+"/../form/model/model_form.model_item.hdf5"
        self.model_form_item = Model_form_item()
        self.model_form_context = Model_form_context()
        self.model_dict = {"line":[None,self.model_file_line]}
        
        
    def getModel(self,type):
        if type=="item":
            return self.model_form_item
        elif type=="context":
            return self.model_form_context
        else:
            return self.getModel(type)

    def encode(self,data,**kwargs):
        return encodeInput([data], word_len=50, word_flag=True,userFool=False)[0]
        return encodeInput_form(data)

    
    def predict(self,form_datas,type):
        if type=="item":
            return self.model_form_item.predict(form_datas)
        elif type=="context":
            return self.model_form_context.predict(form_datas)
        else:
            return self.getModel(type).predict(form_datas)
    

#角色规则
#依据正则给所有无角色的实体赋予角色，给予等于阈值的最低概率
class RoleRulePredictor():
    
    def __init__(self):
        self.pattern_tenderee_left = "(?P<tenderee_left>((采购|招标|项目|竞价|议价|需求|最终|建设|转让|招租|甲|议标|合同主体)(?:人|公司|单位|组织|用户|业主|方|部门)|文章来源|业主名称|需方)(是|为|信息|：|:|\s*$))"
        self.pattern_tenderee_center = "(?P<tenderee_center>(受.{,20}委托))"
        self.pattern_tenderee_right = "(?P<tenderee_right>(\((以下简称)?[\"”]?(招标|采购)(人|单位|机构)\)?)|(^[^.。，,:：](采购|竞价|招标|施工|监理|中标|物资)(公告|公示|项目|结果|招标))|的.*正在进行询比价)"
        
        self.pattern_agency_left = "(?P<agency_left>(代理(?:人|机构|公司|单位|组织)|专业采购机构|集中采购机构|招标机构)(是|为|：|:|[,，]?\s*$)|(受.{,20}委托))"
        self.pattern_agency_right = "(?P<agency_right>(\((以下简称)?[\"”]?(代理)(人|单位|机构)\))|受.*委托)"
        # 2020//11/24 大网站规则 中标关键词添加 选定单位|指定的中介服务机构
        self.pattern_winTenderer_left = "(?P<winTenderer_left>((中标|中选|中价|乙|成交|承做|施工|供货|承包|竞得|受让)(候选)?(人|单位|机构|供应商|方|公司|厂商|商)|(供应商|供货商|服务商|选定单位|指定的中介服务机构)).{,4}[：:是为].{,2}|(第[一1](名|((中标|中选|中价|成交)?(候选)?(人|单位|机构|供应商))))(是|为|：|:|\s*$)|((评审结果|名次|排名)[:：]第?[一1]名?)|(单一来源(采购)?方式向.?$)|((中标|成交)(结果|信息))|(单一来源采购(供应商|供货商|服务商))|((分包|标包).*供应商|供应商名称|服务机构|供方[:：]))"
        self.pattern_winTenderer_center = "(?P<winTenderer_center>第[一1].{,20}[是为]((中标|中选|中价|成交|施工)(人|单位|机构|供应商|公司)|供应商).{,4}[：:是为])"
        self.pattern_winTenderer_right = "(?P<winTenderer_right>[是为\(]((采购(供应商|供货商|服务商)|(第[一1]|预)?(拟?(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司|厂商)))))"
        self.pattern_winTenderer_whole = "(?P<winTenderer_whole>贵公司.*以.*中标|最终由.*竞买成功|经.*[以由].*中标|成交供应商，成交供应商名称：|谈判结果：由.{5,20}供货)"   # 2020//11/24 大网站规则 中标关键词添加 谈判结果：由.{5,20}供货

        self.pattern_winTenderer_location = "(中标|中选|中价|乙|成交|承做|施工|供货|承包|竞得|受让)(候选)?(人|单位|机构|供应商|方|公司|厂商|商)|(供应商|供货商|服务商).{,4}[：:]?$|(第[一1](名|((中标|中选|中价|成交)?(候选)?(人|单位|机构|供应商))))(是|为|：|:|\s*$)|((评审结果|名次|排名)[:：]第?[一1]名?)|(单一来源(采购)?方式向.?$)"

        self.pattern_secondTenderer_left = "(?P<secondTenderer_left>((第[二2](名|((中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司))))(是|为|：|:|\s*$))|((评审结果|名次|排名)[:：]第?[二2]名?))"
        self.pattern_secondTenderer_right = "(?P<secondTenderer_right>[是为\(]第[二2](名|(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司)))"
        
        self.pattern_thirdTenderer_left = "(?P<thirdTenderer_left>(第[三3](名|((中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司))))|((评审结果|名次|排名)[:：]第?[三3]名?))"
        self.pattern_thirdTenderer_right = "(?P<thirdTenderer_right>[是为\(]第[三3](名|(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司)))"
        
        self.dict_list_pattern = {"0":[["L",self.pattern_tenderee_left],
                                      ["C",self.pattern_tenderee_center],
                                      ["R",self.pattern_tenderee_right]],
                                 "1":[["L",self.pattern_agency_left],
                                      ["R",self.pattern_agency_right]],
                                 "2":[["L",self.pattern_winTenderer_left],
                                      ["C",self.pattern_winTenderer_center],
                                      ["R",self.pattern_winTenderer_right],
                                      ["W",self.pattern_winTenderer_whole]],
                                 "3":[["L",self.pattern_secondTenderer_left],
                                      ["R",self.pattern_secondTenderer_right]],
                                 "4":[["L",self.pattern_thirdTenderer_left],
                                      ["R",self.pattern_thirdTenderer_right]]}
        list_pattern = []
        for _k,_v in self.dict_list_pattern.items():
            for _d,_p in _v:
                list_pattern.append(_p)
        self.pattern_whole = "|".join(list_pattern)
        
        self.SET_NOT_TENDERER = set(["人民政府","人民法院","中华人民共和国","人民检察院","评标委员会","中国政府","中国海关"])
        
        self.pattern_money_tenderee = re.compile("投标最高限价|采购计划金额|项目预算|招标金额|采购金额|项目金额|建安费用|采购(单位|人)委托价|限价|拦标价|预算金额")
        self.pattern_money_tenderer = re.compile("((合同|成交|中标|应付款|交易|投标)[）\)]?(总?金额|结果|[单报]?价))|总价|标的基本情况")
        self.pattern_money_tenderer_whole = re.compile("(以金额.*中标)|中标供应商.*单价|以.*元中标")
        self.pattern_money_other = re.compile("代理费|服务费")
        self.pattern_pack = "(([^承](包|标[段号的包]|分?包|包组)编?号?|项目)[:：]?[\(（]?[0-9A-Za-z一二三四五六七八九十]{1,4})[^至]?|(第?[0-9A-Za-z一二三四五六七八九十]{1,4}(包号|标[段号的包]|分?包))|[0-9]个(包|标[段号的包]|分?包|包组)"
        
    def _check_input(self,text, ignore=False):
        if not text:
            return []
        
        if not isinstance(text, list):
            text = [text]
        
        null_index = [i for i, t in enumerate(text) if not t]
        if null_index and not ignore:
            raise Exception("null text in input ")
        
        return text
        
    def predict(self,list_articles,list_sentences,list_entitys,list_codenames,on_value = 0.5):
        

        for article,list_entity,list_sentence,list_codename in zip(list_articles,list_entitys,list_sentences,list_codenames):
            list_name = list_codename["name"]
            list_name = self._check_input(list_name)+[article.title]
            for p_entity in list_entity:


                if p_entity.entity_type in ["org","company"]:
                    #将上下文包含标题的实体概率置为0.6，因为标题中的实体不一定是招标人
                    if str(p_entity.label)=="0":
                        find_flag = False
                        for _sentence in list_sentence:
                            if _sentence.sentence_index==p_entity.sentence_index:
                                _span = spanWindow(tokens=_sentence.tokens,begin_index=p_entity.begin_index,end_index=p_entity.end_index,size=20,center_include=True,word_flag=True,text=p_entity.entity_text)
                                for _name in list_name:
                                    if _name!="" and str(_span[1]+_span[2][:len(str(_name))]).find(_name)>=0:
                                        find_flag = True
                                        if p_entity.values[0]>on_value:
                                            p_entity.values[0] = 0.6+(p_entity.values[0]-0.6)/10
                        if find_flag:
                            continue
                    
                    
                    #只解析角色为无的或者概率低于阈值的
                    if p_entity.label is None:
                        continue
                    role_prob = float(p_entity.values[int(p_entity.label)])
                    if role_prob<on_value or str(p_entity.label)=="5":
                        
                        #将标题中的实体置为招标人
                        _list_name = self._check_input(list_name,ignore=True)
                        find_flag = False
                        for _name in _list_name:
                            if str(_name).find(p_entity.entity_text)>=0:
                                find_flag = True
                                _label = 0
                                p_entity.label = _label
                                p_entity.values[int(_label)] = on_value
                                break
                        #若是实体在标题中，默认为招标人，不进行以下的规则匹配
                        if find_flag:
                            continue
                        
                        for s_index in range(len(list_sentence)):
                            if p_entity.doc_id==list_sentence[s_index].doc_id and p_entity.sentence_index==list_sentence[s_index].sentence_index:
                                tokens = list_sentence[s_index].tokens
                                begin_index = p_entity.begin_index
                                end_index = p_entity.end_index
                                size = 15
                                spans = spanWindow(tokens, begin_index, end_index, size, center_include=True, word_flag=True, use_text=False)
                                #距离
                                list_distance = [100,100,100,100,100]
                                _flag = False


                                #使用正则+距离解决冲突
                                list_spans = [spans[0][-30:],spans[1],spans[2]]
                                for _i_span in range(len(list_spans)):
                                    # print(list_spans[_i_span],p_entity.entity_text)
                                    for _iter in re.finditer(self.pattern_whole,list_spans[_i_span]):
                                        for _group,_v_group in _iter.groupdict().items():
                                            if _v_group is not None and _v_group!="":
                                                # print(_group,_v_group)
                                                _role = _group.split("_")[0]
                                                _direct = _group.split("_")[1]
                                                _label = {"tenderee":0,"agency":1,"winTenderer":2,"secondTenderer":3,"thirdTenderer":4}.get(_role)
                                                if _i_span==0 and _direct=="left":
                                                    _flag = True
                                                    _distance = abs((len(list_spans[_i_span])-_iter.span()[1]))
                                                    list_distance[int(_label)] = min(_distance,list_distance[int(_label)])
                                                if _i_span==1 and _direct=="center":
                                                    _flag = True
                                                    _distance = abs((len(list_spans[_i_span])-_iter.span()[1]))
                                                    list_distance[int(_label)] = min(_distance,list_distance[int(_label)])
                                                if _i_span==2 and _direct=="right":
                                                    _flag = True
                                                    _distance = _iter.span()[0]
                                                    list_distance[int(_label)] = min(_distance,list_distance[int(_label)])



                                # for _key in self.dict_list_pattern.keys():
                                #
                                #     for pattern in self.dict_list_pattern[_key]:
                                #         if pattern[0]=="L":
                                #             for _iter in re.finditer(pattern[1], spans[0][-30:]):
                                #                 _flag = True
                                #                 if len(spans[0])-_iter.span()[1]<list_distance[int(_key)]:
                                #                     list_distance[int(_key)] = len(spans[0])-_iter.span()[1]-(_iter.span()[1]-_iter.span()[0])
                                #
                                #         if pattern[0]=="C":
                                #             if re.search(pattern[1],spans[0]) is None and re.search(pattern[1],spans[2]) is None and re.search(pattern[1],spans[0]+spans[1]+spans[2]) is not None:
                                #                 _flag = True
                                #                 list_distance[int(_key)] = 0
                                #
                                #         if pattern[0]=="R":
                                #             for _iter in re.finditer(pattern[1], spans[2][:30]):
                                #                 _flag = True
                                #                 if _iter.span()[0]<list_distance[int(_key)]:
                                #                     list_distance[int(_key)] = _iter.span()[0]
                                #         if pattern[0]=="W":
                                #             spans = spanWindow(tokens, begin_index, end_index, size=20, center_include=True, word_flag=True, use_text=False)
                                #             for _iter in re.finditer(pattern[1], "".join(spans)):
                                #                 _flag = True
                                #                 if _iter.span()[0]<list_distance[int(_key)]:
                                #                     list_distance[int(_key)] = _iter.span()[0]


                                # print("==",list_distance)
                                #得到结果
                                _label = np.argmin(list_distance)
                                if _flag:
                                    # if _label==2 and min(list_distance[3:])<100:
                                    #     _label += np.argmin(list_distance[3:])+1
                                    if _label in [2,3,4]:
                                        if p_entity.entity_type in ["company","org"]:
                                            p_entity.label = _label
                                            p_entity.values[int(_label)] = on_value+p_entity.values[int(_label)]/10
                                    else:
                                        p_entity.label = _label
                                        p_entity.values[int(_label)] = on_value+p_entity.values[int(_label)]/10
                # if p_entity.entity_type=="location":
                #     for _sentence in list_sentence:
                #         if _sentence.sentence_index==p_entity.sentence_index:
                #             _span = spanWindow(tokens=_sentence.tokens,begin_index=p_entity.begin_index,end_index=p_entity.end_index,size=5,center_include=True,word_flag=True,text=p_entity.entity_text)
                #             if re.search(self.pattern_winTenderer_location,_span[0][-10:]) is not None and re.search("地址|地点",_span[0]) is None:
                #                 p_entity.entity_type="company"
                #                 _label = "2"
                #                 p_entity.label = _label
                #                 p_entity.values = [0]*6
                #                 p_entity.values[int(_label)] = on_value


                #确定性强的特殊修改
                for s_index in range(len(list_sentence)):
                    if p_entity.doc_id==list_sentence[s_index].doc_id and p_entity.sentence_index==list_sentence[s_index].sentence_index:
                        tokens = list_sentence[s_index].tokens
                        begin_index = p_entity.begin_index
                        end_index = p_entity.end_index
                        size = 15
                        spans = spanWindow(tokens, begin_index, end_index, size, center_include=True, word_flag=True, use_text=False)
                        #距离
                        list_distance = [100,100,100,100,100]
                        _flag = False
                        for _key in self.dict_list_pattern.keys():
                            for pattern in self.dict_list_pattern[_key]:
                                if pattern[0]=="W":
                                    spans = spanWindow(tokens, begin_index, end_index, size=30, center_include=True, word_flag=True, use_text=False)
                                    for _iter in re.finditer(pattern[1], spans[0][-10:]+spans[1]+spans[2]):
                                        _flag = True
                                        if _iter.span()[0]<list_distance[int(_key)]:
                                            list_distance[int(_key)] = _iter.span()[0]
                        #得到结果
                        _label = np.argmin(list_distance)
                        if _flag:
                            if _label==2 and min(list_distance[3:])<100:
                                _label += np.argmin(list_distance[3:])+1
                            if _label in [2,3,4]:
                                if p_entity.entity_type in ["company","org"]:
                                    p_entity.label = _label
                                    p_entity.values[int(_label)] = on_value+p_entity.values[int(_label)]/10
                            else:
                                p_entity.label = _label
                                p_entity.values[int(_label)] = on_value+p_entity.values[int(_label)]/10
                if p_entity.entity_type in ["money"]:
                    if str(p_entity.label)=="2":
                        for _sentence in list_sentence:
                            if _sentence.sentence_index==p_entity.sentence_index:
                                _span = spanWindow(tokens=_sentence.tokens,begin_index=p_entity.begin_index,end_index=p_entity.end_index,size=20,center_include=True,word_flag=True,text=p_entity.entity_text)
                                if re.search(self.pattern_money_tenderee,_span[0]) is not None and re.search(self.pattern_money_other,_span[0]) is None:
                                    p_entity.values[0] = 0.8+p_entity.values[0]/10
                                    p_entity.label = 0
                                if re.search(self.pattern_money_tenderer,_span[0]) is not None:
                                    if re.search(self.pattern_money_other,_span[0]) is not None:
                                        if re.search(self.pattern_money_tenderer,_span[0]).span()[1]>re.search(self.pattern_money_other,_span[0]).span()[1]:
                                            p_entity.values[1] = 0.8+p_entity.values[1]/10
                                            p_entity.label = 1
                                    else:
                                        p_entity.values[1] = 0.8+p_entity.values[1]/10
                                        p_entity.label = 1
                                if re.search(self.pattern_money_tenderer_whole,"".join(_span)) is not None and re.search(self.pattern_money_other,_span[0]) is None:
                                    p_entity.values[1] = 0.8+p_entity.values[1]/10
                                    p_entity.label = 1
                                    
            #增加招标金额扩展，招标金额+连续的未识别金额，并且都可以匹配到标段信息，则将为识别的金额设置为招标金额
            list_p = []
            state = 0
            for p_entity in list_entity:
                for _sentence in list_sentence:
                    if _sentence.sentence_index==p_entity.sentence_index:
                        _span = spanWindow(tokens=_sentence.tokens,begin_index=p_entity.begin_index,end_index=p_entity.end_index,size=20,center_include=True,word_flag=True,text=p_entity.entity_text)
                        
                        if state==2:
                            for _p in list_p[1:]:
                                
                                _p.values[0] = 0.8+_p.values[0]/10
                                _p.label = 0
                            state = 0
                            list_p = []
                        
                        if state==0:
                            if p_entity.entity_type in ["money"]:
                                if str(p_entity.label)=="0" and re.search(self.pattern_pack,_span[0]+"-"+_span[2]) is not None:
                                    state = 1
                                    list_p.append(p_entity)
                        elif state==1:
                            if p_entity.entity_type in ["money"]:
                                if str(p_entity.label) in ["0","2"] and re.search(self.pattern_pack,_span[0]+"-"+_span[2]) is not None and re.search(self.pattern_money_other,_span[0]+"-"+_span[2]) is None and p_entity.sentence_index==list_p[0].sentence_index:
                                    list_p.append(p_entity)
                                else:
                                    state = 2
                        
            if len(list_p)>1:
                for _p in list_p[1:]:
                    #print("==",_p.entity_text,_p.sentence_index,_p.label)
                    _p.values[0] = 0.8+_p.values[0]/10
                    _p.label = 0
                state = 0
                list_p = []
                    
                
            for p_entity in list_entity:
                #将属于集合中的不可能是中标人的标签置为无
                if p_entity.entity_text in self.SET_NOT_TENDERER:
                    p_entity.label=5

# 时间类别
class TimePredictor():
    def __init__(self):
        self.sess = tf.Session(graph=tf.Graph())
        self.inputs_code = None
        self.outputs_code = None
        self.input_shape = (2,10,128)
        self.load_model()

    def load_model(self):
        model_path = os.path.dirname(__file__)+'/timesplit_model'
        if self.inputs_code is None:
            log("get model of time")
            with self.sess.as_default():
                with self.sess.graph.as_default():
                    meta_graph_def = tf.saved_model.loader.load(self.sess, tags=["serve"], export_dir=model_path)
                    signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                    signature_def = meta_graph_def.signature_def
                    self.inputs_code = []
                    self.inputs_code.append(
                        self.sess.graph.get_tensor_by_name(signature_def[signature_key].inputs["input0"].name))
                    self.inputs_code.append(
                        self.sess.graph.get_tensor_by_name(signature_def[signature_key].inputs["input1"].name))
                    self.outputs_code = self.sess.graph.get_tensor_by_name(signature_def[signature_key].outputs["outputs"].name)
                    return self.inputs_code, self.outputs_code
        else:
            return self.inputs_code, self.outputs_code

    def search_time_data(self,list_sentences,list_entitys):
        data_x = []
        points_entitys = []
        for list_sentence, list_entity in zip(list_sentences, list_entitys):
            p_entitys = 0
            p_sentences = 0
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type in ['time']:
                    while(p_sentences<len(list_sentence)):
                        sentence = list_sentence[p_sentences]
                        if entity.doc_id == sentence.doc_id and entity.sentence_index == sentence.sentence_index:
                            # left = sentence.sentence_text[max(0,entity.wordOffset_begin-self.input_shape[1]):entity.wordOffset_begin]
                            # right = sentence.sentence_text[entity.wordOffset_end:entity.wordOffset_end+self.input_shape[1]]
                            s = spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=self.input_shape[1])
                            left = s[0]
                            right = s[1]
                            context = [left, right]
                            x = embedding(context, shape=self.input_shape)
                            data_x.append(x)
                            points_entitys.append(entity)
                            break
                        p_sentences += 1
                p_entitys += 1
        if len(points_entitys)==0:
            return None
        data_x = np.transpose(np.array(data_x), (1, 0, 2, 3))
        return [data_x, points_entitys]

    def predict(self, list_sentences,list_entitys):
        datas = self.search_time_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        with self.sess.as_default():
            predict_y = limitRun(self.sess,[self.outputs_code], feed_dict={self.inputs_code[0]:datas[0][0]
                ,self.inputs_code[1]:datas[0][1]})[0]
            for i in range(len(predict_y)):
                entity = points_entitys[i]
                label = np.argmax(predict_y[i])
                values = []
                for item in predict_y[i]:
                    values.append(item)
                    entity.set_Role(label, values)

# 产品字段提取
class ProductPredictor():
    def __init__(self):
        self.sess = tf.Session(graph=tf.Graph())
        self.load_model()

    def load_model(self):
        model_path = os.path.dirname(__file__)+'/product_savedmodel/product.pb'
        with self.sess.as_default():
            with self.sess.graph.as_default():
                output_graph_def = tf.GraphDef()
                with open(model_path, 'rb') as f:
                    output_graph_def.ParseFromString(f.read())
                    tf.import_graph_def(output_graph_def, name='')
                    self.sess.run(tf.global_variables_initializer())
                    self.char_input = self.sess.graph.get_tensor_by_name('CharInputs:0')
                    self.length = self.sess.graph.get_tensor_by_name("Sum:0")
                    self.dropout = self.sess.graph.get_tensor_by_name("Dropout:0")
                    self.logit = self.sess.graph.get_tensor_by_name("logits/Reshape:0")
                    self.tran = self.sess.graph.get_tensor_by_name("crf_loss/transitions:0")

    def predict(self, list_sentences,list_entitys=None, MAX_AREA=5000):
        '''
        预测实体代码，每个句子最多取MAX_AREA个字，超过截断
        :param list_sentences: 多篇公告句子列表,[[一篇公告句子列表],[公告句子列表]]
        :param list_entitys: 多篇公告实体列表
        :param MAX_AREA: 每个句子最多截取多少字
        :return: 把预测出来的实体放进实体类
        '''
        with self.sess.as_default() as sess:
            with self.sess.graph.as_default():
                result = []
                if list_entitys is None:
                    list_entitys = [[] for _ in range(len(list_sentences))]
                for list_sentence, list_entity in zip(list_sentences,list_entitys):
                    if len(list_sentence)==0:
                        result.append({"product":[]})
                        continue
                    list_sentence.sort(key=lambda x:len(x.sentence_text), reverse=True)
                    _begin_index = 0
                    item = {"product":[]}
                    temp_list = []
                    while True:
                        MAX_LEN = len(list_sentence[_begin_index].sentence_text)
                        if MAX_LEN > MAX_AREA:
                            MAX_LEN = MAX_AREA
                        _LEN = MAX_AREA//MAX_LEN
                        chars = process_data([sentence.sentence_text[:MAX_LEN] for sentence in list_sentence[_begin_index:_begin_index+_LEN]])
                        lengths, scores, tran_ = sess.run([self.length, self.logit, self.tran],
                                                          feed_dict={
                                                                    self.char_input: np.asarray(chars),
                                                                    self.dropout: 1.0
                                                                    })
                        batch_paths = decode(scores, lengths, tran_)
                        for sentence, path, length in zip(list_sentence[_begin_index:_begin_index+_LEN],batch_paths, lengths):
                            tags = ''.join([str(it) for it in path[:length]])
                            for it in re.finditer("12*3", tags):
                                start = it.start()
                                end = it.end()
                                _entity = Entity(doc_id=sentence.doc_id, entity_id="%s_%s_%s_%s" % (
                                sentence.doc_id, sentence.sentence_index, start, end),
                                                 entity_text=sentence.sentence_text[start:end],
                                                 entity_type="product", sentence_index=sentence.sentence_index,
                                                 begin_index=0, end_index=0, wordOffset_begin=start,
                                                 wordOffset_end=end)
                                list_entity.append(_entity)
                                temp_list.append(sentence.sentence_text[start:end])
                        item["product"] = list(set(temp_list))
                        result.append(item)
                        if _begin_index+_LEN >= len(list_sentence):
                            break
                        _begin_index += _LEN
                return result

def getSavedModel():
    #predictor = FormPredictor()
    graph = tf.Graph()
    with graph.as_default():
        model = tf.keras.models.load_model("../form/model/model_form.model_item.hdf5",custom_objects={"precision":precision,"recall":recall,"f1_score":f1_score})
        
        #print(tf.graph_util.remove_training_nodes(model))
        tf.saved_model.simple_save(
          tf.keras.backend.get_session(),
          "./h5_savedmodel/",
          inputs={"image": model.input},
          outputs={"scores": model.output}
        )
        
def getBiLSTMCRFModel(MAX_LEN,vocab,EMBED_DIM,BiRNN_UNITS,chunk_tags,weights):
    '''
    model = models.Sequential()
    model.add(layers.Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
    model.add(layers.Bidirectional(layers.LSTM(BiRNN_UNITS // 2, return_sequences=True)))
    crf = CRF(len(chunk_tags), sparse_target=True)
    model.add(crf)
    model.summary()
    model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
    return model
    '''
    input = layers.Input(shape=(None,),dtype="int32")
    if weights is not None:
        embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True,weights=[weights],trainable=True)(input)
    else:
        embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True)(input)
    bilstm = layers.Bidirectional(layers.LSTM(BiRNN_UNITS//2,return_sequences=True))(embedding)
    bilstm_dense = layers.TimeDistributed(layers.Dense(len(chunk_tags)))(bilstm)
    crf = CRF(len(chunk_tags),sparse_target=True)
    crf_out = crf(bilstm_dense)
    model = models.Model(input=[input],output = [crf_out])
    model.summary()
    model.compile(optimizer = 'adam', loss = crf.loss_function, metrics = [crf.accuracy])
    return model

from tensorflow.contrib.crf import crf_log_likelihood
from tensorflow.contrib.layers.python.layers import initializers
def BiLSTM_CRF_tfmodel(sess,embedding_weights):
    '''
    :param embedding_weights: 预训练的字向量矩阵

    '''
    BiRNN_Unit = 100
    chunk_tags = {
        'O': 0,
        'PN_B': 1,
        'PN_M': 2,
        'PN_E': 3,
        'PC_B': 4,
        'PC_M': 5,
        'PC_E': 6,
    }

    def embedding_layer(input,keepprob):
        # 加载预训练的字向量矩阵
        embedding = tf.get_variable(name="embedding",initializer=np.array(embedding_weights, dtype=np.float32),dtype=tf.float32)
        embedding = tf.nn.embedding_lookup(params=embedding,ids=input)
        embedding_drop = tf.nn.dropout(embedding,keepprob)
        return embedding_drop

    def BiLSTM_Layer(input,length):
        with tf.variable_scope("BiLSTM"):
            forward_cell = tf.contrib.rnn.BasicLSTMCell(BiRNN_Unit,state_is_tuple=True)
            backward_cell = tf.contrib.rnn.BasicLSTMCell(BiRNN_Unit,state_is_tuple=True)
        output, _ = tf.nn.bidirectional_dynamic_rnn(forward_cell,backward_cell,input,dtype=tf.float32,sequence_length=length)
        output = tf.concat(output,2)
        return output

    def CRF_layer(input,num_tags,BiRNN_Unit,time_step,keepprob):
        with tf.variable_scope("CRF"):
            with tf.variable_scope("hidden"):
                w_hidden = tf.get_variable(name='w_hidden',shape=(BiRNN_Unit*2,BiRNN_Unit),dtype=tf.float32,
                                           initializer=initializers.xavier_initializer(),regularizer=tf.contrib.layers.l2_regularizer(0.001))
                b_hidden = tf.get_variable(name='b_hidden',shape=(BiRNN_Unit),dtype=tf.float32,initializer=tf.zeros_initializer())
                # print(input)
                input_reshape = tf.reshape(input,shape=(-1,BiRNN_Unit*2))
                hidden = tf.tanh(tf.nn.xw_plus_b(input_reshape,w_hidden,b_hidden))
                hidden = tf.nn.dropout(hidden,keepprob)
            with tf.variable_scope("output"):
                w_output = tf.get_variable(name='w_output',shape=(BiRNN_Unit,num_tags),dtype=tf.float32,initializer=initializers.xavier_initializer(),regularizer=tf.contrib.layers.l2_regularizer(0.001))
                b_output = tf.get_variable(name='b_output',shape=(num_tags),dtype=tf.float32,initializer=tf.zeros_initializer())
                pred = tf.nn.xw_plus_b(hidden,w_output,b_output)
                logits_ = tf.reshape(pred,shape=(-1,time_step,num_tags),name='logits')
        return logits_

    def layer_loss(input,true_target,num_tags,length):
        with tf.variable_scope("crf_loss"):
            trans = tf.get_variable(name='transitons',shape=(num_tags,num_tags),dtype=tf.float32,initializer=initializers.xavier_initializer())
            log_likelihood,trans = crf_log_likelihood(inputs=input,tag_indices=true_target,transition_params=trans,sequence_lengths=length)
            return tf.reduce_mean(-log_likelihood),trans

    with sess.graph.as_default():
        char_input = tf.placeholder(name='char_input',shape=(None,None),dtype=tf.int32)
        target = tf.placeholder(name='target',shape=(None,None),dtype=tf.int32)
        length = tf.placeholder(name='length',shape=(None,),dtype=tf.int32)
        keepprob = tf.placeholder(name='keepprob',dtype=tf.float32)

        _embedding = embedding_layer(char_input,keepprob)
        _shape = tf.shape(char_input)
        batch_size = _shape[0]
        step_size = _shape[-1]
        bilstm = BiLSTM_Layer(_embedding,length)
        _logits = CRF_layer(bilstm,num_tags=len(chunk_tags),BiRNN_Unit=BiRNN_Unit,time_step=step_size,keepprob=keepprob)
        crf_loss,trans = layer_loss(_logits,true_target=target,num_tags=len(chunk_tags),length=length)
        global_step = tf.Variable(0,trainable=False)
        with tf.variable_scope("optimizer"):
            opt = tf.train.AdamOptimizer(0.002)
            grads_vars = opt.compute_gradients(crf_loss)
            capped_grads_vars = [[tf.clip_by_value(g,-5,5),v] for g,v in grads_vars]
            train_op = opt.apply_gradients(capped_grads_vars,global_step)
            return char_input,_logits,target,keepprob,length,crf_loss,trans,train_op

import h5py
def h5_to_graph(sess,graph,h5file):
    
    f = h5py.File(h5file,'r')   #打开h5文件 
    def getValue(v):
        _value = f["model_weights"]
        list_names = str(v.name).split("/")
        for _index in range(len(list_names)):
            print(v.name)
            if _index==1:
                _value = _value[list_names[0]]
            _value = _value[list_names[_index]]
        return _value.value
            
    def _load_attributes_from_hdf5_group(group, name):
        """Loads attributes of the specified name from the HDF5 group.
    
        This method deals with an inherent problem
        of HDF5 file which is not able to store
        data larger than HDF5_OBJECT_HEADER_LIMIT bytes.
    
        # Arguments
            group: A pointer to a HDF5 group.
            name: A name of the attributes to load.
    
        # Returns
            data: Attributes data.
        """
        if name in group.attrs:
            data = [n.decode('utf8') for n in group.attrs[name]]
        else:
            data = []
            chunk_id = 0
            while ('%s%d' % (name, chunk_id)) in group.attrs:
                data.extend([n.decode('utf8')
                            for n in group.attrs['%s%d' % (name, chunk_id)]])
                chunk_id += 1
        return data
    
    def readGroup(gr,parent_name,data):
        for subkey in gr:
            print(subkey)
            if parent_name!=subkey:
                if parent_name=="":
                    _name = subkey
                else:
                    _name = parent_name+"/"+subkey
            else:
                _name = parent_name
            if str(type(gr[subkey]))=="<class 'h5py._hl.group.Group'>":
                readGroup(gr[subkey],_name,data)
            else:
                data.append([_name,gr[subkey].value])
                print(_name,gr[subkey].shape)
                
    
    layer_names = _load_attributes_from_hdf5_group(f["model_weights"], 'layer_names')
    list_name_value = []
    readGroup(f["model_weights"], "", list_name_value)
    '''
    for k, name in enumerate(layer_names):
        g = f["model_weights"][name]
        weight_names = _load_attributes_from_hdf5_group(g, 'weight_names')
        #weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
        for weight_name in weight_names:
            list_name_value.append([weight_name,np.asarray(g[weight_name])])
    '''
    for name_value in list_name_value:
        name = name_value[0]
        '''
        if re.search("dense",name) is not None:
            name = name[:7]+"_1"+name[7:]
        '''
        value = name_value[1]
        print(name,graph.get_tensor_by_name(name),np.shape(value))
        sess.run(tf.assign(graph.get_tensor_by_name(name),value))

def initialize_uninitialized(sess):
    global_vars          = tf.global_variables()
    is_not_initialized   = sess.run([tf.is_variable_initialized(var) for var in global_vars])
    not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f]
    
    adam_vars = []
    for _vars in not_initialized_vars:
        if re.search("Adam",_vars.name) is not None:
            adam_vars.append(_vars)
 
    print([str(i.name) for i in adam_vars]) # only for testing
    if len(adam_vars):
        sess.run(tf.variables_initializer(adam_vars))
    
      
def save_codename_model():
    # filepath = "../projectCode/models/model_project_"+str(60)+"_"+str(200)+".hdf5"
    filepath = "../projectCode/models_tf/59-L0.471516189943-F0.8802154826344823-P0.8789179683459191-R0.8815168335321886/model.ckpt"
    vocabpath = "../projectCode/models/vocab.pk"
    classlabelspath = "../projectCode/models/classlabels.pk"
    # vocab = load(vocabpath)
    # class_labels = load(classlabelspath)
    w2v_matrix = load('codename_w2v_matrix.pk')
    graph = tf.get_default_graph()
    with graph.as_default() as g:
        ''''''
        # model = getBiLSTMCRFModel(None, vocab, 60, 200, class_labels,weights=None)
        #model = models.load_model(filepath,custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score,"CRF":CRF,"loss":CRF.loss_function})
        
        sess = tf.Session(graph=g)
        # sess = tf.keras.backend.get_session()
        char_input, logits, target, keepprob, length, crf_loss, trans, train_op = BiLSTM_CRF_tfmodel(sess, w2v_matrix)
        #with sess.as_default():
        sess.run(tf.global_variables_initializer())
        # print(sess.run("time_distributed_1/kernel:0"))
        # model.load_weights(filepath)
        saver = tf.train.Saver()
        saver.restore(sess, filepath)

        # print("logits",sess.run(logits))
        
        # print("#",sess.run("time_distributed_1/kernel:0"))

        # x = load("codename_x.pk")
        #y = model.predict(x)
        # y = sess.run(model.output,feed_dict={model.input:x})
        
        # for item in np.argmax(y,-1):
        #     print(item)
        tf.saved_model.simple_save(
                                    sess,
                                    "./codename_savedmodel_tf/",
                                    inputs={"inputs": char_input,
                                            "inputs_length":length,
                                            'keepprob':keepprob},
                                    outputs={"logits": logits,
                                             "trans":trans}
        )
        
    
def save_role_model():
    '''
    @summary: 保存model为savedModel，部署到PAI平台上调用
    '''
    model_role = PREMPredict().model_role
    with model_role.graph.as_default():
        model = model_role.getModel()
        sess = tf.Session(graph=model_role.graph)
        print(type(model.input))
        
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, model_role.graph, model_role.model_role_file)
        model = model_role.getModel()
        
        tf.saved_model.simple_save(sess,
                                   "./role_savedmodel/",
                                   inputs={"input0":model.input[0],
                                           "input1":model.input[1],
                                           "input2":model.input[2]},
                                   outputs={"outputs":model.output}
                                   )
    
def save_money_model():
    model_money = PREMPredict().model_money
    with model_money.graph.as_default():
        model = model_money.getModel()
        sess = tf.Session(graph=model_money.graph)
        model.summary()
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, model_money.graph, model_money.model_money_file)
        tf.saved_model.simple_save(sess,
                                   "./money_savedmodel/",
                                   inputs = {"input0":model.input[0],
                                             "input1":model.input[1],
                                             "input2":model.input[2]},
                                   outputs = {"outputs":model.output}
                                   )
    

def save_person_model():
    model_person = EPCPredict().model_person
    with model_person.graph.as_default():
        
        x = load("person_x.pk")
        _data = np.transpose(np.array(x),(1,0,2,3))
        model = model_person.getModel()
        
        sess = tf.Session(graph=model_person.graph)
        with sess.as_default():
            
            sess.run(tf.global_variables_initializer())
            model_person.load_weights()
        
        
        #h5_to_graph(sess, model_person.graph, model_person.model_person_file)
        
        predict_y = sess.run(model.output,feed_dict={model.input[0]:_data[0],model.input[1]:_data[1]})
        #predict_y = model.predict([_data[0],_data[1]])
        print(np.argmax(predict_y,-1))
        
        tf.saved_model.simple_save(sess,
                                   "./person_savedmodel/",
                                   inputs={"input0":model.input[0],
                                           "input1":model.input[1]},
                                   outputs = {"outputs":model.output})
    
def save_form_model():
    model_form = FormPredictor()
    with model_form.graph.as_default():
        model = model_form.getModel("item")
        sess = tf.Session(graph=model_form.graph)
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, model_form.graph, model_form.model_file_item)
        tf.saved_model.simple_save(sess,
                                   "./form_savedmodel/",
                                   inputs={"inputs":model.input},
                                   outputs = {"outputs":model.output})
    
def save_codesplit_model():
    filepath_code = "../projectCode/models/model_code.hdf5"
    
    
    graph = tf.Graph()
    with graph.as_default():
        model_code = models.load_model(filepath_code, custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
        sess = tf.Session()
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, graph, filepath_code)
        tf.saved_model.simple_save(sess,
                                   "./codesplit_savedmodel/",
                                   inputs={"input0":model_code.input[0],
                                           "input1":model_code.input[1],
                                           "input2":model_code.input[2]},
                                   outputs={"outputs":model_code.output})

def save_timesplit_model():
    filepath = '../time/model_label_time_classify.model.hdf5'
    with tf.Graph().as_default() as graph:
        time_model = models.load_model(filepath, custom_objects={'precision': precision, 'recall': recall, 'f1_score': f1_score})
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            h5_to_graph(sess, graph, filepath)
            tf.saved_model.simple_save(sess,
                                       "./timesplit_model/",
                                       inputs={"input0":time_model.input[0],
                                               "input1":time_model.input[1]},
                                       outputs={"outputs":time_model.output})


if __name__=="__main__":
    #save_role_model()
    # save_codename_model()
    #save_money_model()
    #save_person_model()
    #save_form_model()
    #save_codesplit_model()
    # save_timesplit_model()
    '''
    with tf.Session(graph=tf.Graph()) as sess:
        from tensorflow.python.saved_model import tag_constants
        meta_graph_def = tf.saved_model.loader.load(sess, [tag_constants.SERVING], "./person_savedModel")
        graph = tf.get_default_graph()
        signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
        signature = meta_graph_def.signature_def
        input0 = sess.graph.get_tensor_by_name(signature[signature_key].inputs["input0"].name)
        input1 = sess.graph.get_tensor_by_name(signature[signature_key].inputs["input1"].name)
        outputs = sess.graph.get_tensor_by_name(signature[signature_key].outputs["outputs"].name)
        x = load("person_x.pk")
        _data = np.transpose(x,[1,0,2,3])
        y = sess.run(outputs,feed_dict={input0:_data[0],input1:_data[1]})
        print(np.argmax(y,-1))
    '''