import nltk #line:1
from nltk .corpus import stopwords #line:2
from nltk .tokenize import word_tokenize #line:3
from nltk .tag import pos_tag #line:4
from pydantic import BaseModel #line:5
from faker import Faker #line:6
import os #line:7
import pandas as pd #line:8
import json #line:9
import random #line:10
import requests #line:11
nltk .download ('averaged_perceptron_tagger')#line:13
nltk .download ('punkt')#line:14
nltk .download ('stopwords')#line:15
class textAnonyms (BaseModel ):#line:18
    originalText :str #line:19
    textFormat :str #line:20
stop_words =set (stopwords .words ('french'))#line:23
liste_pays =["afghanistan","afrique du sud","albanie","algérie","allemagne","andorre","angola","antigua-et-barbuda","arabie saoudite","argentine","arménie","aruba","australie","autriche","azerbaïdjan","bahamas","bahreïn","bangladesh","barbade","belgique","belize","bélarus","bénin","bhoutan","birmanie","bolivie","bosnie-herzégovine","botswana","brésil","brunéi","bulgarie","burkina faso","burundi","cambodge","cameroun","canada","cap-vert","chili","chine","chypre","colombie","comores","corée du nord","corée du sud","costa rica","côte d'ivoire","croatie","cuba","curaçao","danemark","djibouti","dominique","egypte","el salvador","émirats arabes unis","équateur","érythrée","espagne","estonie","éthiopie","fidji","finlande","france","gabon","gambie","géorgie","ghana","grèce","grenade","guatemala","guinée","guinée équatoriale","guinée-bissau","guyana","haïti","honduras","hongrie","inde","indonésie","irak","iran","irlande","islande","israël","italie","jamaïque","japon","jordanie","kazakhstan","kenya","kirghizistan","kiribati","kosovo","koweït","laos","lesotho","lettonie","liban","libéria","libye","liechtenstein","lituanie","luxembourg","macédoine du nord","madagascar","malaisie","malawi","maldives","mali","malte","maroc","marshall","maurice","mauritanie","mexique","micronésie","moldavie","monaco","mongolie","monténégro","mozambique","namibie","nauru","nepal","nicaragua","niger","nigeria","niue","norvège","nouvelle-zélande","oman","ouganda","ouzbékistan","pakistan","palaos","panama","papouasie nouvelle-guinée","paraguay","pays-bas","pérou","philippines","pologne","portugal","qatar","république centrafricaine","république démocratique du congo","république dominicaine","république du congo","république tchèque","roumanie","royaume-uni","russie","rwanda","saint-christophe-et-niévès","saint-marin","saint-martin","saint-vincent-et-les-grenadines","sainte-lucie","salomon","salvador","samoa","são tomé-et-principe","sénégal","serbie","seychelles","sierra leone","singapour","slovaquie","slovénie","somalie","soudan","soudan du sud","sri lanka","suède","suisse","surinam","swaziland","syrie","tadjikistan","tanzanie","tchad","thaïlande","timor oriental","togo","tonga","trinité-et-tobago","tunisie","turkménistan","turquie","tuvalu","ukraine","uruguay","vanuatu","vatican","venezuela","vietnam","yémen","zambie","zimbabwe"]#line:24
faker =Faker (["fr_FR"])#line:25
url ="https://raw.githubusercontent.com/high54/Communes-France-JSON/master/france.json"#line:27
response =requests .get (url )#line:29
villes =response .json ()#line:30
def anonymiser_mot (O0O0O0OO0OO00O0OO :textAnonyms ):#line:34
    try :#line:35
        O00OO0OOOO0OOO0O0 =pd .read_csv ("words.csv",dtype ={"original":str ,"anonymous":str })#line:36
        if (O0O0O0OO0OO00O0OO .originalText =="FM"):#line:38
            OOOOOOOO0OOO00O0O ="DAB+"#line:39
        elif (O0O0O0OO0OO00O0OO .originalText =="DAB+"):#line:40
            OOOOOOOO0OOO00O0O ="FM"#line:41
        elif (O0O0O0OO0OO00O0OO .originalText .lower ()=="iqoya"):#line:42
            OOOOOOOO0OOO00O0O ="codec audio"#line:43
        elif (O0O0O0OO0OO00O0OO .textFormat =="PERSON"):#line:44
            if (cherche_ville (O0O0O0OO0OO00O0OO .originalText .upper ())):#line:46
                OOOOOOOO0OOO00O0O =random .choice (["TOULON","NANTES","MONTPELLIER","CHAMBOURCY","NANTERRE","GRENOBLE","LYON"])#line:47
            elif (cherche_chaine (O0O0O0OO0OO00O0OO .originalText .upper ())):#line:48
                OOOOOOOO0OOO00O0O =random .choice (["TF1","M6","BFMTV" "FRANCE5","FRANCE2"])#line:49
            else :#line:50
                OOOOOOOO0OOO00O0O =random .choice (["PAUL","JEAN","PHILIPPE","PIERRE","MARC","DAVID","GUILLAUME"])#line:51
        elif (O0O0O0OO0OO00O0OO .textFormat =="DATE"):#line:52
            OOOOOOOO0OOO00O0O =faker .date ()#line:53
        elif (O0O0O0OO0OO00O0OO .textFormat =="LOCATION"):#line:54
            OOOOOOOO0OOO00O0O =faker .address ()#line:55
        elif (O0O0O0OO0OO00O0OO .textFormat =="NUMBER"):#line:56
            if (int (O0O0O0OO0OO00O0OO .originalText )<24 ):#line:57
                OOOOOOOO0OOO00O0O =faker .numerify (text ='#')#line:58
            else :#line:59
                OOOOOOOO0OOO00O0O =str (faker .random_int (min =0 ,max =(int (O0O0O0OO0OO00O0OO .originalText )-1 )))#line:60
        elif (O0O0O0OO0OO00O0OO .textFormat =="COUNTRY"):#line:61
            OOOOOOOO0OOO00O0O =faker .country ()#line:62
        elif (O0O0O0OO0OO00O0OO .textFormat =="ORGANIZATION"):#line:63
            OOOOOOOO0OOO00O0O =random .choice (["ORANGE","SAFRAN","BOUYGUES","FREE"])#line:64
        while any (O00OO0OOOO0OOO0O0 ["anonymous"]==OOOOOOOO0OOO00O0O ):#line:68
            OOOOOOOO0OOO00O0O =faker .first_name ()#line:69
        O00OO0OOOO0OOO0O0 =pd .concat ([O00OO0OOOO0OOO0O0 ,pd .DataFrame ([[O0O0O0OO0OO00O0OO .originalText ,OOOOOOOO0OOO00O0O ]],columns =["original","anonymous"])])#line:71
        O00OO0OOOO0OOO0O0 .to_csv ("words.csv",index =False )#line:72
        return OOOOOOOO0OOO00O0O #line:74
    except Exception as OO0O0OO00O0O00OOO :#line:75
        return O0O0O0OO0OO00O0OO .originalText #line:76
def cherche_chaine (OOO00000O0000O0O0 ):#line:79
    OOO00000O0000O0O0 =OOO00000O0000O0O0 .upper ()#line:80
    O0O0OOO0O0OOO00O0 =["C8","CNEWS","TF1","M6","CSTAR","BFM","F5"]#line:82
    for O00O00O0OO00OO00O in O0O0OOO0O0OOO00O0 :#line:83
        if O00O00O0OO00OO00O in OOO00000O0000O0O0 :#line:84
            return True #line:85
            break #line:86
    return False #line:88
def cherche_ville (OO0O0O000O00OO0OO ):#line:91
    OO0O0O000O00OO0OO =OO0O0O000O00OO0OO .upper ()#line:92
    for OO0OOO0O000OOO000 in villes :#line:93
        if OO0O0O000O00OO0OO in OO0OOO0O000OOO000 ["Nom_commune"]:#line:94
            return True #line:95
            break #line:96
    return False #line:98
def desanonymiser_mot (OOOO0OOO00OO0O00O ):#line:100
    O00OO00O0OOOO0OO0 =pd .read_csv ("words.csv",dtype ={"original":str ,"anonymous":str })#line:101
    if not O00OO00O0OOOO0OO0 .empty :#line:102
        OOOO000OO00OO0000 =O00OO00O0OOOO0OO0 [O00OO00O0OOOO0OO0 ["anonymous"]==OOOO0OOO00OO0O00O ]["original"]#line:103
        if not OOOO000OO00OO0000 .empty :#line:104
            return OOOO000OO00OO0000 .iloc [0 ]#line:105
    return None #line:106
def initialiser ():#line:108
    O0OOOOOOOO0OOOO0O ="words.csv"#line:109
    if os .path .exists (O0OOOOOOOO0OOOO0O ):#line:111
        os .remove (O0OOOOOOOO0OOOO0O )#line:112
    O000OO0000OOO0OO0 =pd .DataFrame (columns =["original","anonymous"])#line:114
    O000OO0000OOO0OO0 .to_csv (O0OOOOOOOO0OOOO0O ,index =False )#line:116
def anonymiser_paragraphe (OO00OOOO0OO0OO000 ):#line:121
    OOO0OOO0OO0OO0000 =OO00OOOO0OO0OO000 #line:123
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace (".",". ")#line:124
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace (",",", ")#line:125
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("0H","0 H")#line:126
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("1H","1 H")#line:127
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("2H","2 H")#line:128
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("3H","3 H")#line:129
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("4H","4 H")#line:130
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("5H","5 H")#line:131
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("6H","6 H")#line:132
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("7H","7 H")#line:133
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("8H","8 H")#line:134
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("9H","9 H")#line:135
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("0h","0 h")#line:136
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("1h","1 h")#line:137
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("2h","2 h")#line:138
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("3h","3 h")#line:139
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("4h","4 h")#line:140
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("5h","5 h")#line:141
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("6h","6 h")#line:142
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("7h","7 h")#line:143
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("8h","8 h")#line:144
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("9h","9 h")#line:145
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("0F","0 F ")#line:146
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("1F","1 F ")#line:147
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("2F","2 F ")#line:148
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("3F","3 F ")#line:149
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("4F","4 F ")#line:150
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("5F","5 F ")#line:151
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("6F","6 F ")#line:152
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("7F","7 F ")#line:153
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("8F","8 F ")#line:154
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("9F","9 F ")#line:155
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("0f","0 f ")#line:156
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("1f","1 f ")#line:157
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("2f","2 f ")#line:158
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("3f","3 f ")#line:159
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("4f","4 f ")#line:160
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("5f","5 f ")#line:161
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("6f","6 f ")#line:162
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("7f","7 f ")#line:163
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("8f","8 f ")#line:164
    OOO0OOO0OO0OO0000 =OOO0OOO0OO0OO0000 .replace ("9f","9 f ")#line:165
    O0000O0O000OO0OOO =word_tokenize (OOO0OOO0OO0OO0000 ,language ="french")#line:166
    OOO0OO0OOOOO0OO0O =pos_tag (O0000O0O000OO0OOO )#line:167
    OOOO0O00OO0O0OO00 =[]#line:168
    OO0OOOO0OO00000O0 =set (stopwords .words ('french'))#line:170
    O0O0O0OOO0O0O00O0 =["h","mon","ma","HF","mes","ton","ta","tes","son","sa","ses","notre","votre","leur","leurs","merci","alors","fh","hf","intervention","j'ai","télégéstion","télégestion","absence","énergie","radio"]#line:171
    OO0OOOO0OO00000O0 .update (O0O0O0OOO0O0O00O0 )#line:172
    OOOO000OOO00OOOOO =0 #line:173
    for OO0OO0O00000OO0O0 ,OOOOO0OOOO000OO0O in OOO0OO0OOOOO0OO0O :#line:174
        OOOO000OOO00OOOOO =OOOO000OOO00OOOOO +1 #line:175
        if OO0OO0O00000OO0O0 .lower ()in liste_pays :#line:176
            OOOO0O00OO0O0OO00 .append (("COUNTRY",OO0OO0O00000OO0O0 ))#line:177
        elif OOOOO0OOOO000OO0O =="NNP"and "DS"in OO0OO0O00000OO0O0 or "LA"in OO0OO0O00000OO0O0 :#line:178
            OOOO0O00OO0O0OO00 .append (("NUMBER",OO0OO0O00000OO0O0 ))#line:179
        elif OOOOO0OOOO000OO0O =="NNP"and OO0OO0O00000OO0O0 .isupper ()and OO0OO0O00000OO0O0 .lower ()not in OO0OOOO0OO00000O0 and len (OO0OO0O00000OO0O0 )>1 :#line:180
            OOOO0O00OO0O0OO00 .append (("ORGANIZATION",OO0OO0O00000OO0O0 ))#line:181
        elif OOOOO0OOOO000OO0O =="NNP"and OO0OO0O00000OO0O0 .lower ()not in OO0OOOO0OO00000O0 and OOOO000OOO00OOOOO >1 and len (OO0OO0O00000OO0O0 )>1 :#line:182
            OOOO0O00OO0O0OO00 .append (("PERSON",OO0OO0O00000OO0O0 ))#line:183
        elif OOOOO0OOOO000OO0O =="CD"and "/"in OO0OO0O00000OO0O0 :#line:184
            OOOO0O00OO0O0OO00 .append (("DATE",OO0OO0O00000OO0O0 ))#line:185
        elif OOOOO0OOOO000OO0O =="CD"and ":"not in OO0OO0O00000OO0O0 :#line:186
            OOOO0O00OO0O0OO00 .append (("NUMBER",OO0OO0O00000OO0O0 ))#line:187
        elif OOOOO0OOOO000OO0O =="NNP"and OO0OO0O00000OO0O0 .lower ()not in OO0OOOO0OO00000O0 and OOOO000OOO00OOOOO >1 and len (OO0OO0O00000OO0O0 )>1 :#line:188
            OOOO0O00OO0O0OO00 .append (("LOCATION",OO0OO0O00000OO0O0 ))#line:189
    for OO00O000OO0000O0O ,O0O00000O000O0OO0 in OOOO0O00OO0O0OO00 :#line:193
        OOOO00OOO00O0000O =textAnonyms (originalText =O0O00000O000O0OO0 ,textFormat =OO00O000OO0000O0O )#line:194
        OO00OOOO0OO0OO000 =OO00OOOO0OO0OO000 .replace (O0O00000O000O0OO0 ,anonymiser_mot (OOOO00OOO00O0000O ))#line:195
    OO00OOOO0OO0OO000 =OO00OOOO0OO0OO000 .replace ("-","/")#line:197
    OO00OOOO0OO0OO000 =OO00OOOO0OO0OO000 .replace (" / "," - ")#line:198
    return OO00OOOO0OO0OO000 #line:199
def desanonymiser_paragraphe (O00OOO0O00000OOO0 ):#line:201
    OOOOOOO0OO0OO00OO =pd .read_csv ("words.csv",dtype ={"original":str ,"anonymous":str })#line:204
    for OO0O0O00O0OOO00O0 ,OOO0O000O000O00OO in OOOOOOO0OO0OO00OO .iterrows ():#line:205
        O00OOO0O00000OOO0 =O00OOO0O00000OOO0 .replace (OOO0O000O000O00OO ["anonymous"],OOO0O000O000O00OO ["original"])#line:207
    return O00OOO0O00000OOO0 #line:208
