main_NER.py

import pdb
import config_utils as cf
import requests
import sys
import urllib.parse
import numpy as np
from collections import OrderedDict
import argparse
from ensemble.utils.common import *
import json

#WORD_POS = 1
#TAG_POS = 2
#MASK_TAG = "__entity__"
DISPATCH_MASK_TAG = "entity"
DESC_HEAD = "PIVOT_DESCRIPTORS:"
#TYPE2_AMB = "AMB2-"
TYPE2_AMB = ""
DUMMY_DESCS=10
DEFAULT_ENTITY_MAP = "entity_types_consolidated.txt"

#RESET_POS_TAG='RESET'
SPECIFIC_TAG=":__entity__"



#noun_tags = ['NFP','JJ','NN','FW','NNS','NNPS','JJS','JJR','NNP','POS','CD']
#cap_tags = ['NFP','JJ','NN','FW','NNS','NNPS','JJS','JJR','NNP','PRP']

def read_common_descs(file_name):
    common_descs = {}
    with open(file_name) as fp:
        for line in fp:
            common_descs[line.strip()] = 1
    print("Common descs for filtering read:",len(common_descs))
    return common_descs

def read_entity_map(file_name):
    emap = {}
    with open(file_name) as fp:
        for line in fp:
            line = line.rstrip('\n')
            entities = line.split()
            if (len(entities) == 1):
                assert(entities[0] not in emap)
                emap[entities[0]] = entities[0]
            else:
                assert(len(entities) == 2)
                entity_arr = entities[1].split('/')
                if (entities[0] not in emap):
                    emap[entities[0]] = entities[0]
                for entity in entity_arr:
                    assert(entity not in emap)
                    emap[entity] = entities[0]
    print("Entity map:",len(emap))
    return emap

class UnsupNER:
    def __init__(self):
        print("NER service handler started")
        self.pos_server_url  = cf.read_config()["POS_SERVER_URL"]
        self.desc_server_url  = cf.read_config()["DESC_SERVER_URL"]
        self.entity_server_url  = cf.read_config()["ENTITY_SERVER_URL"]
        self.common_descs = read_common_descs(cf.read_config()["COMMON_DESCS_FILE"])
        self.entity_map = read_entity_map(cf.read_config()["EMAP_FILE"])
        self.rfp = open("log_results.txt","a")
        self.dfp = open("log_debug.txt","a")
        print(self.pos_server_url)
        print(self.desc_server_url)
        print(self.entity_server_url)
        np.set_printoptions(suppress=True) #this suppresses exponential representation when np is used to round
        if (cf.read_config()["SUPPRESS_UNTAGGED"] == "1"):
            self.suppress_untagged = True
        else:
            self.suppress_untagged = False #This is disabled in full debug text mode


    #This is bad hack for prototyping - parsing from text output as opposed to json
    def extract_POS(self,text):
        arr = text.split('\n')
        if (len(arr) > 0):
            start_pos = 0
            for i,line in enumerate(arr):
                if (len(line) > 0):
                    start_pos += 1
                    continue
                else:
                    break
            #print(arr[start_pos:])
            terms_arr = []
            for i,line in enumerate(arr[start_pos:]):
                terms = line.split('\t')
                if (len(terms) == 5):
                    #print(terms)
                    terms_arr.append(terms)
            return terms_arr

    def normalize_casing(self,sent):
        sent_arr = sent.split()
        ret_sent_arr = []
        for i,word in enumerate(sent_arr):
            if (len(word) > 1):
                norm_word = word[0] + word[1:].lower()
            else:
                norm_word = word[0]
            ret_sent_arr.append(norm_word)
        return ' '.join(ret_sent_arr)

    def tag_sentence_service(self,text,full_sentence_tag):
        if (full_sentence_tag):
            ret_str = self.tag_sentence(text,self.rfp,self.dfp,True)
        else:
            entity_arr,span_arr,terms_arr,ner_str,debug_str = self.tag_sentence(text,self.rfp,self.dfp,False)
            ret_str = ner_str + "\nDEBUG_OUTPUT\n" + '\n'.join(debug_str)
        return ret_str

    def dictify_ner_response(self,ner_str):
        arr = ner_str.split('\n')
        ret_dict = OrderedDict()
        count = 1
        ref_indices_arr = []
        for line in arr:
            terms = line.split()
            if (len(terms) == 2):
                ret_dict[count] = {"term":terms[0],"e":terms[1]}
                if (terms[1] != "O" and terms[1].startswith("B_")):
                        ref_indices_arr.append(count)
                count += 1
        return ret_dict,ref_indices_arr

    def pool_confidences(self,ci_entities,ci_confidences,ci_subtypes,cs_entities,cs_confidences,cs_subtypes,debug_str_arr):
        main_classes = {}
        assert(len(cs_entities) ==  len(cs_confidences))
        assert(len(cs_subtypes) ==  len(cs_entities))
        assert(len(ci_entities) ==  len(ci_confidences))
        assert(len(ci_subtypes) ==  len(ci_entities))
        #Pool entity classes across CI and CS
        for e,c in zip(ci_entities,ci_confidences):
            e_base = e.split('[')[0]
            main_classes[e_base] = float(c)
        for e,c in zip(cs_entities,cs_confidences):
            e_base = e.split('[')[0]
            if (e_base in main_classes):
                main_classes[e_base] += float(c)
            else:
                main_classes[e_base] = float(c)
        final_sorted_d = OrderedDict(sorted(main_classes.items(), key=lambda kv: kv[1], reverse=True))
        main_dist = self.convert_positive_nums_to_dist(final_sorted_d)
        main_classes_arr = list(final_sorted_d.keys())
        print(main_classes_arr)
        print(main_dist)
        #Pool subtypes across CI and CS for a particular entity class
        subtype_factors = {}
        for e_class in final_sorted_d:
            if e_class in cs_subtypes:
                stypes = cs_subtypes[e_class]
                if (e_class not in subtype_factors):
                    subtype_factors[e_class] = {}
                for st in stypes:
                    if (st in subtype_factors[e_class]):
                        subtype_factors[e_class][st] += stypes[st]
                    else:
                        subtype_factors[e_class][st] = stypes[st]
            if e_class in ci_subtypes:
                stypes = ci_subtypes[e_class]
                if (e_class not in subtype_factors):
                    subtype_factors[e_class] = {}
                for st in stypes:
                    if (st in subtype_factors[e_class]):
                        subtype_factors[e_class][st] += stypes[st]
                    else:
                        subtype_factors[e_class][st] = stypes[st]
        sorted_subtype_factors = {}
        for e_class in subtype_factors:
            stypes = subtype_factors[e_class]
            final_sorted_d = OrderedDict(sorted(stypes.items(), key=lambda kv: kv[1], reverse=True))
            stypes_dist = self.convert_positive_nums_to_dist(final_sorted_d)
            stypes_class_arr = list(final_sorted_d.keys())
            sorted_subtype_factors[e_class] = {"stypes":stypes_class_arr,"dist":stypes_dist}
        pooled_results = OrderedDict()
        assert(len(main_classes_arr) == len(main_dist))
        d_str_arr = []
        d_str_arr.append("***CONSOLIDATED ENTITY:")
        for e,c in zip(main_classes_arr,main_dist):
            pooled_results[e] = {"e":e,"confidence":c}
            d_str_arr.append(e + " " + str(c))
            stypes_dict = sorted_subtype_factors[e]
            pooled_st = OrderedDict()
            for st,sd in zip(stypes_dict["stypes"],stypes_dict["dist"]):
                pooled_st[st] = sd
            pooled_results[e]["stypes"] = pooled_st
        debug_str_arr.append(' '.join(d_str_arr))
        return pooled_results









    def init_entity_info(self,entity_info_dict,index):
        curr_term_dict = OrderedDict()
        entity_info_dict[index] = curr_term_dict
        curr_term_dict["ci"] = OrderedDict()
        curr_term_dict["ci"]["entities"] = []
        curr_term_dict["ci"]["descs"] = []
        curr_term_dict["cs"] = OrderedDict()
        curr_term_dict["cs"]["entities"] = []
        curr_term_dict["cs"]["descs"] = []




    #This now does specific tagging if there is a __entity__ in sentence; else does full tagging. TBD.
    #TBD. Make response params same regardlesss of output format. Now it is different
    def tag_sentence(self,sent,rfp,dfp,json_output):
        dfp.write("\n\n++++-------------------------------\n")
        dfp.write("NER_INPUT: " + sent + "\n")
        debug_str_arr = []
        entity_info_dict = OrderedDict()
        #sent = self.normalize_casing(sent)
        #print("Caps normalized sentence:", sent)
        if (SPECIFIC_TAG in sent):
            terms_arr = set_POS_based_on_entities(sent)
        else:
            url = self.pos_server_url  + sent.replace('"','\'')
            r = self.dispatch_request(url)
            terms_arr = self.extract_POS(r.text)
        masked_sent_arr,span_arr = generate_masked_sentences(terms_arr)
        masked_sent_arr,span_arr = filter_common_noun_spans(span_arr,masked_sent_arr,terms_arr,self.common_descs)
        singleton_sentences,singleton_spans_arr = self.gen_single_phrase_sentences(terms_arr,masked_sent_arr,span_arr,rfp,dfp)
        #Find CI predictions for ALL masked predictios in sentence
        ci_predictions = self.find_ci_entities(singleton_sentences,singleton_spans_arr,debug_str_arr,entity_info_dict)
        #Find CS predictions for ALL masked predictios in sentence. Use the CI predictions from previous step to
        #pool
        detected_entities_arr,ner_str,full_pooled_results = self.find_cs_entities(sent,terms_arr,masked_sent_arr,span_arr,rfp,dfp,debug_str_arr,ci_predictions,entity_info_dict)
        assert(len(detected_entities_arr) == len(entity_info_dict))
        print("--------")
        if (json_output):
            if (len(detected_entities_arr) != len(entity_info_dict)):
                if (len(entity_info_dict) == 0):
                    self.init_entity_info(entity_info_dict,index)
                    entity_info_dict[1]["cs"]["entities"].append([{"e":"O","confidence":1}])
                    entity_info_dict[1]["ci"]["entities"].append([{"e":"O","confidence":1}])
            ret_dict,ref_indices_arr  = self.dictify_ner_response(ner_str) #Convert ner string to a dictionary for json output
            assert(len(ref_indices_arr)  == len(detected_entities_arr))
            assert(len(entity_info_dict)  == len(detected_entities_arr))
            cs_aux_dict = OrderedDict()
            ci_aux_dict = OrderedDict()
            pooled_pred_dict = OrderedDict()
            count = 0
            assert(len(full_pooled_results) == len(detected_entities_arr))
            for e,c,p in zip(detected_entities_arr,entity_info_dict,full_pooled_results):
                val = entity_info_dict[c]
                #cs_aux_dict[ref_indices_arr[count]] = {"e":e,"cs_distribution":val["cs"]["entities"],"cs_descs":val["cs"]["descs"]}
                pooled_pred_dict[ref_indices_arr[count]] = {"e": e, "cs_distribution": list(p.values())}
                cs_aux_dict[ref_indices_arr[count]] = {"e":e,"cs_descs":val["cs"]["descs"]}
                #ci_aux_dict[ref_indices_arr[count]] = {"ci_distribution":val["ci"]["entities"],"ci_descs":val["ci"]["descs"]}
                ci_aux_dict[ref_indices_arr[count]] = {"ci_descs":val["ci"]["descs"]}
                count += 1
            #print(ret_dict)
            #print(aux_dict)
            final_ret_dict = {"total_terms_count":len(ret_dict),"detected_entity_phrases_count":len(detected_entities_arr),"ner":ret_dict,"entity_distribution":pooled_pred_dict,"cs_prediction_details":cs_aux_dict,"ci_prediction_details":ci_aux_dict,"debug":debug_str_arr}
            json_str = json.dumps(final_ret_dict,indent = 4)
            #print (json_str)
            #with open("single_debug.txt","w") as fp:
            #    fp.write(json_str)

            dfp.write('\n'.join(debug_str_arr))
            dfp.write("\n\nEND-------------------------------\n")
            dfp.flush()
            return json_str
        else:
            print(detected_entities_arr)
            debug_str_arr.append("NER_FINAL_RESULTS: " + ' '.join(detected_entities_arr))
            print("--------")
            dfp.write('\n'.join(debug_str_arr))
            dfp.write("\n\nEND-------------------------------\n")
            dfp.flush()
            return detected_entities_arr,span_arr,terms_arr,ner_str,debug_str_arr

    def masked_word_first_letter_capitalize(self,entity):
        arr = entity.split()
        ret_arr = []
        for term in arr:
            if (len(term) > 1 and term[0].islower() and term[1].islower()):
                ret_arr.append(term[0].upper() + term[1:])
            else:
                ret_arr.append(term)
        return ' '.join(ret_arr)


    def gen_single_phrase_sentences(self,terms_arr,masked_sent_arr,span_arr,rfp,dfp):
        sentence_template = "%s is a entity"
        print(span_arr)
        sentences = []
        singleton_spans_arr  = []
        run_index = 0
        entity  = ""
        singleton_span = []
        while (run_index < len(span_arr)):
            if (span_arr[run_index] == 1):
                while (run_index < len(span_arr)):
                    if (span_arr[run_index] == 1):
                        #print(terms_arr[run_index][WORD_POS],end=' ')
                        if (len(entity) == 0):
                            entity = terms_arr[run_index][WORD_POS]
                        else:
                            entity = entity + " " + terms_arr[run_index][WORD_POS]
                        singleton_span.append(1)
                        run_index += 1
                    else:
                        break
                #print()
                for i in sentence_template.split():
                    if (i != "%s"):
                        singleton_span.append(0)
                entity = self.masked_word_first_letter_capitalize(entity)
                sentence = sentence_template % entity
                sentences.append(sentence)
                singleton_spans_arr.append(singleton_span)
                print(sentence)
                print(singleton_span)
                entity = ""
                singleton_span = []
            else:
                run_index += 1
        return sentences,singleton_spans_arr


    def find_ci_entities(self,masked_sent_arr,span_arr,debug_str_arr,entity_info_dict):
        term_index = 1
        ci_predictions = []
        for dummy,masked_sent in enumerate(masked_sent_arr):
            print(masked_sent)
            debug_str_arr.append(masked_sent)
            #entity_info_dict["masked_sent"].append(masked_sent)
            descs = self.get_descriptors_for_masked_position(masked_sent,True)
            self.init_entity_info(entity_info_dict,term_index)
            entities,confidences,subtypes = self.get_entities_for_masked_position(descs,debug_str_arr,entity_info_dict[term_index]["ci"])
            ci_predictions.append({"entities":entities,"confidences":confidences,"subtypes":subtypes})
            term_index += 1
        return ci_predictions



    #We have multiple masked versions of a single sentence. Tag each one of them
    #and create a complete tagged version for a sentence
    def find_cs_entities(self,sent,terms_arr,masked_sent_arr,span_arr,rfp,dfp,debug_str_arr,ci_predictions,entity_info_dict):
        #print(sent)
        print(span_arr)
        dfp.write(sent + "\n")
        dfp.write(str(span_arr) + "\n")
        term_index = 1
        detected_entities_arr = []
        full_pooled_results = []
        for index,masked_sent in enumerate(masked_sent_arr):
            ci_entities = ci_predictions[index]["entities"]
            ci_confidences = ci_predictions[index]["confidences"]
            ci_subtypes = ci_predictions[index]["subtypes"]
            masked_sent = ' '.join(masked_sent)
            print(masked_sent)
            debug_str_arr.append("\n++++++ SENT: " + masked_sent)
            dfp.write(masked_sent + "\n")
            descs = self.get_descriptors_for_masked_position(masked_sent,False)
            dfp.write(str(descs) + "\n")
            if (len(descs) > 0):
                cs_entities,cs_confidences,cs_subtypes = self.get_entities_for_masked_position(descs,debug_str_arr,entity_info_dict[term_index]["cs"])
            else:
                cs_entities = []
                cs_confidences = []
                cs_subtypes = []
            dfp.write(str(cs_entities) + "\n")
            pooled_results = self.pool_confidences(ci_entities,ci_confidences,ci_subtypes,cs_entities,cs_confidences,cs_subtypes,debug_str_arr)
            self.fill_detected_entities(detected_entities_arr,pooled_results) #just picks the top prediction
            full_pooled_results.append(pooled_results)
            #self.old_resolve_entities(i,singleton_entities,detected_entities_arr) #This decides how to pick entities given CI and CS predictions
            term_index += 1
        #out of the full loop over sentences. Now create NER sentence
        ner_str = self.emit_sentence_entities(sent,terms_arr,detected_entities_arr,span_arr,rfp) #just outputs results in NER Conll format
        dfp.flush()
        return detected_entities_arr,ner_str,full_pooled_results


    def fill_detected_entities(self,detected_entities_arr,entities):
        if (len(entities) > 0):
            top_e_class = next(iter(entities))
            top_subtype = next(iter(entities[top_e_class]["stypes"]))
            if (top_e_class != top_subtype):
                top_prediction = top_e_class + "[" + top_subtype + "]"
            else:
                top_prediction = top_e_class
            detected_entities_arr.append(top_prediction)
        else:
            detected_entities_arr.append("OTHER")


    def fill_detected_entities_old(self,detected_entities_arr,entities,pan_arr):
        entities_dict = {}
        count = 1
        for i in entities:
            cand = i.split("-")
            for j in cand:
                terms = j.split("/")
                for k in terms:
                    if (k not in entities_dict):
                        entities_dict[k] = 1.0/count
                    else:
                        entities_dict[k] += 1.0/count
            count += 1
        final_sorted_d = OrderedDict(sorted(entities_dict.items(), key=lambda kv: kv[1], reverse=True))
        first = "OTHER"
        for first in final_sorted_d:
            break
        detected_entities_arr.append(first)

    #Contextual entity is picked as first candidate before context independent candidate
    def old_resolve_entities(self,index,singleton_entities,detected_entities_arr):
        if (singleton_entities[index].split('[')[0] != detected_entities_arr[index].split('[')[0]):
            if (singleton_entities[index].split('[')[0] != "OTHER" and detected_entities_arr[index].split('[')[0] != "OTHER"):
                detected_entities_arr[index] = detected_entities_arr[index] + "/" +  singleton_entities[index]
            elif (detected_entities_arr[index].split('[')[0] == "OTHER"):
                detected_entities_arr[index] =  singleton_entities[index]
            else:
                pass
        else:
           #this is the case when both CI and CS entity type match. Since the subtypes are already ordered, just merge(CS/CI,CS/CI...) the two picking unique subtypes
            main_entity = detected_entities_arr[index].split('[')[0]
            cs_arr = detected_entities_arr[index].split('[')[1].rstrip(']').split(',')
            ci_arr = singleton_entities[index].split('[')[1].rstrip(']').split(',')
            cs_arr_len  = len(cs_arr)
            ci_arr_len  = len(ci_arr)
            max_len = ci_arr_len if ci_arr_len > cs_arr_len else cs_arr_len
            merged_unique_subtype_dict = OrderedDict()
            for i in range(cs_arr_len):
                if (i < cs_arr_len and cs_arr[i] not in merged_unique_subtype_dict):
                    merged_unique_subtype_dict[cs_arr[i]] = 1
                if (i < ci_arr_len and ci_arr[i] not in merged_unique_subtype_dict):
                    merged_unique_subtype_dict[ci_arr[i]] = 1
            new_subtypes_str = ','.join(list(merged_unique_subtype_dict.keys()))
            detected_entities_arr[index] =  main_entity + '[' + new_subtypes_str + ']'






    def emit_sentence_entities(self,sent,terms_arr,detected_entities_arr,span_arr,rfp):
        print("Final result")
        ret_str = ""
        for i,term in enumerate(terms_arr):
            print(term[WORD_POS],' ',end='')
        print()
        sent_arr = sent.split()
        assert(len(terms_arr) == len(span_arr))
        entity_index = 0
        i = 0
        in_span = False
        while (i < len(span_arr)):
            if (span_arr[i] == 0):
                tag = "O"
                if (in_span):
                    in_span = False
                    entity_index += 1
            else:
                if (in_span):
                    tag = "I_" + detected_entities_arr[entity_index]
                else:
                    in_span = True
                    tag = "B_" + detected_entities_arr[entity_index]
            rfp.write(terms_arr[i][WORD_POS] + ' ' + tag + "\n")
            ret_str = ret_str + terms_arr[i][WORD_POS] + ' ' + tag + "\n"
            print(tag + ' ',end='')
            i += 1
        print()
        rfp.write("\n")
        ret_str += "\n"
        rfp.flush()
        return ret_str



    #This is just a trivial optimizartion to not have to send integers to get descriptors.
    def match_templates(self,masked_sent):
        words = masked_sent.split()
        words_count = len(words)
        if (len(words) == 4 and words[words_count-1] == "entity" and words[words_count -2] == "a" and words[words_count -3] == "is"  and words[0].isnumeric()): #only integers skipped
            dummy_arr = []
            for i in range(DUMMY_DESCS):
                dummy_arr.append("two")
                dummy_arr.append("0")
            return dummy_arr
        else:
            return None




    def get_descriptors_for_masked_position(self,masked_sent,usecls):
        masked_sent = masked_sent.replace(MASK_TAG,DISPATCH_MASK_TAG)
        ret_val = self.match_templates(masked_sent)
        if (ret_val != None):
            return ret_val
        desc_arr = []
        if (len(masked_sent.split()) > 1):
            usecls_option = "1/" if usecls else "0/"
            r = self.dispatch_request(self.desc_server_url+usecls_option + str(masked_sent))
            desc_arr = self.extract_descs(r.text)
        print(desc_arr)
        return desc_arr

    def dispatch_request(self,url):
        max_retries = 10
        attempts = 0
        while True:
            try:
                r = requests.get(url,timeout=1000)
                if (r.status_code == 200):
                    return r
            except:
                print("Request:", url, " failed. Retrying...")
            attempts += 1
            if (attempts >= max_retries):
                print("Request:", url, " failed")
                break

    def convert_positive_nums_to_dist(self,final_sorted_d):
        factors = list(final_sorted_d.values()) #convert dict values to an array
        factors = list(map(float, factors))
        total = float(sum(factors))
        if (total == 0):
            total = 1
            factors[0] = 1 #just make the sum 100%. This a boundary case for numbers for instance
        factors = np.array(factors)
        factors = factors/total
        factors = np.round(factors,4)
        return factors

    def aggregate_entities(self,entities,desc_weights,debug_str_arr,entity_info_dict_entities):
        ''' Given a masked position, whose entity we are trying to determine,
            First get descriptors for that postion 2*N array [desc1,score1,desc2,score2,...]
            Then for each descriptor, get entity predictions which is an array 2*N of the form [e1,score1,e2,score2,...] where e1 could be DRUG/DISEASE and score1 is 10/8 etc.
            In this function we aggregate each unique entity prediction (e.g. DISEASE) by summing up its weighted scores across all N predictions.
            The result factor array is normalized to create a probability distribution
        '''
        count = len(entities)
        assert(count %2 == 0)
        aggregate_entities = {}
        i = 0
        subtypes = {}
        while (i < count):
            curr_counts = entities[i+1].split('/')
            curr_e = self.map_entities(entities[i].split('/'),curr_counts,subtypes)
            assert(len(curr_e) <= len(curr_counts)) # can be less if untagges is skipped
            curr_counts_sum = sum(map(int,curr_counts))
            curr_counts_sum = 1 if curr_counts_sum == 0 else curr_counts_sum
            for j in range(len(curr_e)):
                if (self.skip_untagged(curr_e[j])):
                    continue
                if (curr_e[j] not in aggregate_entities):
                    aggregate_entities[curr_e[j]] = (float(curr_counts[j])/curr_counts_sum)*float(desc_weights[i+1])
                    #aggregate_entities[curr_e[j]] = float(desc_weights[i+1])
                else:
                    aggregate_entities[curr_e[j]] += (float(curr_counts[j])/curr_counts_sum)*float(desc_weights[i+1])
                    #aggregate_entities[curr_e[j]] += float(desc_weights[i+1])
            i += 2
        final_sorted_d = OrderedDict(sorted(aggregate_entities.items(), key=lambda kv: kv[1], reverse=True))
        if (len(final_sorted_d) == 0): #Case where all terms are tagged OTHER
            final_sorted_d = {"OTHER":1}
            subtypes["OTHER"] = {"OTHER":1}
        factors = self.convert_positive_nums_to_dist(final_sorted_d)
        ret_entities = list(final_sorted_d.keys())
        confidences = factors.tolist()
        print(ret_entities)
        sorted_subtypes = self.sort_subtypes(subtypes)
        ret_entities = self.update_entities_with_subtypes(ret_entities,sorted_subtypes)
        print(ret_entities)
        debug_str_arr.append(" ")
        debug_str_arr.append(' '.join(ret_entities))
        print(confidences)
        assert(len(confidences) == len(ret_entities))
        arr = []
        for e,c in zip(ret_entities,confidences):
            arr.append({"e":e,"confidence":c})
        entity_info_dict_entities.append(arr)
        debug_str_arr.append(' '.join([str(x) for x in confidences]))
        debug_str_arr.append("\n\n")
        return ret_entities,confidences,subtypes


    def sort_subtypes(self,subtypes):
        sorted_subtypes =  OrderedDict()
        for ent in subtypes:
            final_sorted_d = OrderedDict(sorted(subtypes[ent].items(), key=lambda kv: kv[1], reverse=True))
            sorted_subtypes[ent]  = list(final_sorted_d.keys())
        return sorted_subtypes

    def update_entities_with_subtypes(self,ret_entities,subtypes):
        new_entities = []

        for ent in ret_entities:
            #if (len(ret_entities) == 1):
            #    new_entities.append(ent) #avoid creating a subtype for a single case
            #    return new_entities
            if (ent in subtypes):
                new_entities.append(ent + '[' + ','.join(subtypes[ent]) + ']')
            else:
                new_entities.append(ent)
        return new_entities

    def skip_untagged(self,term):
        if (self.suppress_untagged == True and (term == "OTHER" or term == "UNTAGGED_ENTITY")):
                return True
        return False


    def map_entities(self,arr,counts_arr,subtypes_dict):
        ret_arr = []
        index = 0
        for i in arr:
            if (self.skip_untagged(i)):
                continue
            ret_arr.append(self.entity_map[i])
            #if (i != self.entity_map[i]):
            if (True):
                if (self.entity_map[i] not in subtypes_dict):
                    subtypes_dict[self.entity_map[i]] = {}
                if (i not in subtypes_dict[self.entity_map[i]]):
                    #subtypes_dict[self.entity_map[i]][i] = int(counts_arr[index])
                    subtypes_dict[self.entity_map[i]][i] = 1 #just count the number of occurrence of subtypes as opposed to their counts in clusters. This is to avoid cluster context overwhelming the current sentence context. Consider using this as a fractional score of total sense counts once labeling is mature
                else:
                    #subtypes_dict[self.entity_map[i]][i] += int(counts_arr[index])
                    subtypes_dict[self.entity_map[i]][i] += 1
            index += 1
        return ret_arr


    def get_entities_for_masked_position(self,descs,debug_str_arr,entity_info_dict):
        param = ' '.join(descs[::2]) #send only the descriptors - not the neighborhood scores
        r = self.dispatch_request(self.entity_server_url+str(param))
        entities = r.text.split()
        print(entities)
        debug_combined_arr =[]
        desc_arr =[]
        assert(len(descs) %2 == 0)
        assert(len(entities) %2 == 0)
        index = 0
        for d,e in zip(descs,entities):
            debug_combined_arr.append(d + " " + e)
            if (index % 2 == 0):
                temp_dict = OrderedDict()
                temp_dict["d"] = d
                temp_dict["e"] = e
            else:
                temp_dict["mlm"] = d
                temp_dict["l_score"] = e
                desc_arr.append(temp_dict)
            index += 1
        debug_str_arr.append(', '.join(debug_combined_arr))
        entity_info_dict["descs"] = desc_arr
        #debug_str_arr.append(' '.join(entities))
        assert(len(entities) == len(descs))
        entities,confidences,subtypes = self.aggregate_entities(entities,descs,debug_str_arr,entity_info_dict["entities"])
        return entities,confidences,subtypes


   #This is again a bad hack for prototyping purposes - extracting fields from a raw text output as opposed to a structured output like json
    def extract_descs(self,text):
        arr = text.split('\n')
        desc_arr = []
        if (len(arr) > 0):
            for i,line in enumerate(arr):
                if (line.startswith(DESC_HEAD)):
                    terms = line.split(':')
                    desc_arr = ' '.join(terms[1:]).strip().split()
                    break
        return desc_arr


    def generate_masked_sentences(self,terms_arr):
        size = len(terms_arr)
        sentence_arr = []
        span_arr = []
        i = 0
        while (i < size):
            term_info = terms_arr[i]
            if (term_info[TAG_POS] in noun_tags):
                skip = self.gen_sentence(sentence_arr,terms_arr,i)
                i +=  skip
                for j in range(skip):
                    span_arr.append(1)
            else:
                i += 1
                span_arr.append(0)
        #print(sentence_arr)
        return sentence_arr,span_arr

    def gen_sentence(self,sentence_arr,terms_arr,index):
        size = len(terms_arr)
        new_sent = []
        for prefix,term in enumerate(terms_arr[:index]):
            new_sent.append(term[WORD_POS])
        i = index
        skip = 0
        while (i < size):
            if (terms_arr[i][TAG_POS] in noun_tags):
                skip += 1
                i += 1
            else:
                break
        new_sent.append(MASK_TAG)
        i = index + skip
        while (i < size):
            new_sent.append(terms_arr[i][WORD_POS])
            i += 1
        assert(skip != 0)
        sentence_arr.append(new_sent)
        return skip








def run_test(file_name,obj):
    rfp = open("results.txt","w")
    dfp = open("debug.txt","w")
    with open(file_name) as fp:
        count = 1
        for line in fp:
            if (len(line) > 1):
                print(str(count) + "] ",line,end='')
                obj.tag_sentence(line,rfp,dfp)
                count += 1
    rfp.close()
    dfp.close()


def tag_single_entity_in_sentence(file_name,obj):
    rfp = open("results.txt","w")
    dfp = open("debug.txt","w")
    sfp = open("se_results.txt","w")
    with open(file_name) as fp:
        count = 1
        for line in fp:
            if (len(line) > 1):
                print(str(count) + "] ",line,end='')
                #entity_arr,span_arr,terms_arr,ner_str,debug_str = obj.tag_sentence(line,rfp,dfp,False) # False for json output
                json_str = obj.tag_sentence(line,rfp,dfp,True) # True for json output
                pdb.set_trace()
                #print("*******************:",terms_arr[span_arr.index(1)][WORD_POS].rstrip(":"),entity_arr[0])
                #sfp.write(terms_arr[span_arr.index(1)][WORD_POS].rstrip(":") + " " + entity_arr[0] + "\n")
                count += 1
                sfp.flush()
    rfp.close()
    sfp.close()
    dfp.close()






def test_canned_sentences(obj):
    rfp = open("results.txt","w")
    dfp = open("debug.txt","w")
    obj.tag_sentence("Her hypophysitis secondary to ipilimumab was well managed with supplemental hormones",rfp,dfp,True)
    obj.tag_sentence("In Seattle:__entity__ , Pete Incaviglia 's grand slam with one out in the sixth snapped a tie and lifted the Baltimore Orioles past the Seattle           Mariners , 5-2 .",rfp,dfp,True)
    obj.tag_sentence("engineer",rfp,dfp,True)
    obj.tag_sentence("Austin:__entity__ called",rfp,dfp,True)
    obj.tag_sentence("ajit rajasekharan is an engineer",rfp,dfp,True)
    obj.tag_sentence("Paul Erdős died at 83",rfp,dfp,True)
    obj.tag_sentence("Imatinib mesylate is a drug and is used to treat nsclc",rfp,dfp,True)
    obj.tag_sentence("In Seattle , Pete Incaviglia 's grand slam with one out in the sixth snapped a tie and lifted the Baltimore Orioles past the Seattle           Mariners , 5-2 .",rfp,dfp,True)
    obj.tag_sentence("It was Incaviglia 's sixth grand slam and 200th homer of his career .",rfp,dfp,True)
    obj.tag_sentence("Add Women 's singles , third round Lisa Raymond ( U.S. ) beat Kimberly Po ( U.S. ) 6-3 6-2 .",rfp,dfp,True)
    obj.tag_sentence("1880s marked the beginning of Jazz",rfp,dfp,True)
    obj.tag_sentence("He flew from New York to SFO",rfp,dfp,True)
    obj.tag_sentence("Lionel Ritchie was popular in the 1980s",rfp,dfp,True)
    obj.tag_sentence("Lionel Ritchie was popular in the late eighties",rfp,dfp,True)
    obj.tag_sentence("John Doe flew from New York to Rio De Janiro via Miami",rfp,dfp,True)
    obj.tag_sentence("He felt New York has a chance to win this year's competition",rfp,dfp,True)
    obj.tag_sentence("Bandolier - Budgie ' , a free itunes app for ipad , iphone and ipod touch , released in December 2011 , tells the story of the making of Bandolier in the band 's own words - including an extensive audio interview with Burke Shelley",rfp,dfp,True)
    obj.tag_sentence("Fyodor Mikhailovich Dostoevsky was treated for Parkinsons",rfp,dfp,True)
    obj.tag_sentence("In humans mutations in Foxp2 leads to verbal dyspraxia",rfp,dfp,True)
    obj.tag_sentence("The recent spread of Corona virus flu from China to Italy,Iran, South Korea and Japan has caused global concern",rfp,dfp,True)
    obj.tag_sentence("Hotel California topped the singles chart",rfp,dfp,True)
    obj.tag_sentence("Elon Musk said Telsa will open a manufacturing plant in Europe",rfp,dfp,True)
    obj.tag_sentence("He flew from New York to SFO",rfp,dfp,True)
    obj.tag_sentence("After studies at Hofstra University , He worked for New York Telephone before He was elected to the New York State Assembly to represent the 16th District in Northwest Nassau County ",rfp,dfp,True)
    obj.tag_sentence("Everyday he rode his bicycle from Rajakilpakkam to Tambaram",rfp,dfp,True)
    obj.tag_sentence("If he loses Saturday , it could devalue his position as one of the world 's great boxers , \" Panamanian Boxing Association President Ramon     Manzanares said .",rfp,dfp,True)
    obj.tag_sentence("West Indian all-rounder Phil Simmons took four for 38 on Friday as Leicestershire beat Somerset by an innings and 39 runs in two days to take over at the head of the county championship .",rfp,dfp,True)
    obj.tag_sentence("they are his friends ",rfp,dfp,True)
    obj.tag_sentence("they flew from Boston to Rio De Janiro and had a mocha",rfp,dfp,True)
    obj.tag_sentence("he flew from Boston to Rio De Janiro and had a mocha",rfp,dfp,True)
    obj.tag_sentence("X,Y,Z are medicines",rfp,dfp,True)
    rfp.close()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='main NER for a single model ',formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('-input', action="store", dest="input",default="",help='Input file required for run options batch,single')
    parser.add_argument('-option', action="store", dest="option",default="canned",help='Valid options are canned,batch,single. canned - test few canned sentences used in medium artice. batch - tag sentences in input file. Entities to be tagged are determing used POS tagging to find noun phrases. specific - tag specific entities in input file. The tagged word or phrases needs to be of the form w1:__entity_ w2:__entity_ Example:Her hypophysitis:__entity__ secondary to ipilimumab was well managed with supplemental:__entity__ hormones:__entity__')
    results = parser.parse_args()

    obj = UnsupNER()
    if (results.option == "canned"):
        test_canned_sentences(obj)
    elif (results.option == "batch"):
        if (len(results.input) == 0):
            print("Input file needs to be specified")
        else:
            run_test(results.input,obj)
            print("Tags and sentences are written in results.txt and debug.txt")
    elif (results.option == "specific"):
        if (len(results.input) == 0):
            print("Input file needs to be specified")
        else:
            tag_single_entity_in_sentence(results.input,obj)
            print("Tags and sentences are written in results.txt and debug.txt")
    else:
        print("Invalid argument:\n")
        parser.print_help()