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+ "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ"
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1086
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1090
+ "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ",
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+ "ĠĠĠĠĠĠ"
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1093
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1094
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1095
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1098
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1102
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1105
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1106
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1107
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1108
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1110
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1112
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1113
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1114
+ "Ā",
1115
+ "?"
1116
+ ],
1117
+ [
1118
+ "Ġ",
1119
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1121
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1122
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1123
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1124
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1125
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1126
+ "Ā",
1127
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1130
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1133
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1134
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1135
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1136
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1137
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1138
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1141
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1142
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1143
+ "ĠĠĠ"
1144
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1145
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1146
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1147
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1149
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1150
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1152
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1153
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1154
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1158
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1162
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1165
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1166
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+ "ĠĠĠĠĠĠ"
1168
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1169
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1170
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1171
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1172
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1173
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1174
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1178
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1179
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1180
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1181
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1182
+ "Ā?",
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1185
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1186
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1189
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1190
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1192
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1193
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1194
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1198
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1201
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1202
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1204
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1205
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1206
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1207
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1208
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1209
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1210
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1211
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1212
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1213
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1214
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1216
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1217
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1218
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1220
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1221
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1222
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1223
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1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
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1234
+ "ā",
1235
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1237
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1238
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1239
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1240
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1241
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1242
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1243
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1244
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1245
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1246
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1248
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1249
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1250
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1251
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1252
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1253
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1254
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1255
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1257
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1258
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1260
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1261
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1262
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1264
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1265
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1266
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1267
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1269
+ [
1270
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+ "%"
1272
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1273
+ [
1274
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1277
+ [
1278
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1279
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1281
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1282
+ "Ă",
1283
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1285
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1286
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1287
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1288
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1289
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1290
+ "ą",
1291
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1292
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1293
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1294
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1298
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1300
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1301
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1302
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1303
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1304
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1305
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1306
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1307
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1308
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1332
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1334
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1345
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1346
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1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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1363
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1366
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1367
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1368
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1369
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1370
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1372
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1373
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1374
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1376
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1378
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1382
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1386
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1388
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1389
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1390
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1391
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1392
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1393
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1394
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1395
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1398
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1402
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1404
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1406
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1407
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1408
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1410
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1414
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1418
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1419
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1422
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1426
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1430
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1434
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1438
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1442
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1450
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1466
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1470
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1474
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1478
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1482
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1526
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1530
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1554
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1558
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1562
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1566
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1570
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1578
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1582
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1586
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1590
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1594
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1598
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1614
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1622
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1626
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1630
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1634
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1638
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1646
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1654
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1658
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1660
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1662
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1666
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1670
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1674
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1678
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1682
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1686
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1690
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1694
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1698
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1702
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1710
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1714
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1716
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1718
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1730
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1734
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1746
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1750
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1754
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1758
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1762
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1766
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1770
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1774
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1778
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+ [
4430
+ "@ě",
4431
+ "+ĕ"
4432
+ ],
4433
+ [
4434
+ "@ĝ",
4435
+ "*"
4436
+ ],
4437
+ [
4438
+ "@-ě",
4439
+ "ę@ā"
4440
+ ],
4441
+ [
4442
+ "@(Ĝ",
4443
+ "Ě@ā"
4444
+ ],
4445
+ [
4446
+ "āĀ@",
4447
+ "ďĄā"
4448
+ ],
4449
+ [
4450
+ "<(",
4451
+ "Ĕ=ĀĀ"
4452
+ ],
4453
+ [
4454
+ "ĆĀ?",
4455
+ "ĚąĀ"
4456
+ ],
4457
+ [
4458
+ "Ā@đ",
4459
+ "Āā"
4460
+ ],
4461
+ [
4462
+ "@\"Ę",
4463
+ "@,ā"
4464
+ ],
4465
+ [
4466
+ "<%Ĝ-",
4467
+ "đĀ"
4468
+ ],
4469
+ [
4470
+ "@'Ğ/",
4471
+ "/Ā"
4472
+ ],
4473
+ [
4474
+ "@'Ĕ@",
4475
+ ")Ā"
4476
+ ],
4477
+ [
4478
+ "ĂĀ@ĕ",
4479
+ "Āā"
4480
+ ],
4481
+ [
4482
+ "ąā@Ď",
4483
+ "Ğā"
4484
+ ],
4485
+ [
4486
+ "ĈĄ?ď",
4487
+ "Ĕā"
4488
+ ],
4489
+ [
4490
+ "ēĂ@Ā",
4491
+ "(ā"
4492
+ ],
4493
+ [
4494
+ "!",
4495
+ "/"
4496
+ ],
4497
+ [
4498
+ "$",
4499
+ "#"
4500
+ ],
4501
+ [
4502
+ "&",
4503
+ "\""
4504
+ ],
4505
+ [
4506
+ "&",
4507
+ "#"
4508
+ ],
4509
+ [
4510
+ "(",
4511
+ "Ę"
4512
+ ],
4513
+ [
4514
+ "+",
4515
+ "Ė"
4516
+ ],
4517
+ [
4518
+ ":",
4519
+ "'"
4520
+ ],
4521
+ [
4522
+ ">",
4523
+ "Ė"
4524
+ ],
4525
+ [
4526
+ ">",
4527
+ "'\""
4528
+ ],
4529
+ [
4530
+ "?",
4531
+ "\""
4532
+ ],
4533
+ [
4534
+ "?",
4535
+ "-"
4536
+ ],
4537
+ [
4538
+ "Ā",
4539
+ "ă"
4540
+ ],
4541
+ [
4542
+ "Ā",
4543
+ "Ā@ĕ"
4544
+ ],
4545
+ [
4546
+ "Ą",
4547
+ "."
4548
+ ],
4549
+ [
4550
+ "ą",
4551
+ "*"
4552
+ ],
4553
+ [
4554
+ "ą",
4555
+ "ĝ"
4556
+ ],
4557
+ [
4558
+ "ą",
4559
+ "Ā?ė"
4560
+ ],
4561
+ [
4562
+ "Ĉ",
4563
+ "ą"
4564
+ ],
4565
+ [
4566
+ "ċ",
4567
+ "ĠĠĠĠ"
4568
+ ],
4569
+ [
4570
+ "Ď",
4571
+ "Āā"
4572
+ ],
4573
+ [
4574
+ "đ",
4575
+ "Ā@"
4576
+ ],
4577
+ [
4578
+ "Ē",
4579
+ "@Ā"
4580
+ ],
4581
+ [
4582
+ "Ė",
4583
+ "@Ā"
4584
+ ],
4585
+ [
4586
+ "ĝ",
4587
+ "Ă"
4588
+ ],
4589
+ [
4590
+ "Ġ",
4591
+ "3"
4592
+ ],
4593
+ [
4594
+ "Ġğ",
4595
+ "Ĝ"
4596
+ ],
4597
+ [
4598
+ "ĀĀ",
4599
+ "Ğ"
4600
+ ],
4601
+ [
4602
+ "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ",
4603
+ "Ġ"
4604
+ ],
4605
+ [
4606
+ "Ġ#",
4607
+ "!"
4608
+ ],
4609
+ [
4610
+ "ĠĜ",
4611
+ "!"
4612
+ ],
4613
+ [
4614
+ "Ĕā",
4615
+ "?"
4616
+ ],
4617
+ [
4618
+ "Ā/",
4619
+ "ĝ"
4620
+ ],
4621
+ [
4622
+ "@#",
4623
+ "\""
4624
+ ],
4625
+ [
4626
+ "ĈĀ@",
4627
+ "đ"
4628
+ ],
4629
+ [
4630
+ "@ě",
4631
+ ")ĝ"
4632
+ ],
4633
+ [
4634
+ "ĂĀ?Ė",
4635
+ "đā"
4636
+ ],
4637
+ [
4638
+ "Ą?ď",
4639
+ "ęā"
4640
+ ],
4641
+ [
4642
+ "@/",
4643
+ "đ@*"
4644
+ ],
4645
+ [
4646
+ "?,",
4647
+ "Ě"
4648
+ ],
4649
+ [
4650
+ ";Ĉ",
4651
+ "<"
4652
+ ],
4653
+ [
4654
+ "<'",
4655
+ "Ě"
4656
+ ],
4657
+ [
4658
+ "Ğ&",
4659
+ "+Ā"
4660
+ ],
4661
+ [
4662
+ ">)Ę",
4663
+ "?"
4664
+ ],
4665
+ [
4666
+ "Ďā?Ĕ",
4667
+ "ėą"
4668
+ ],
4669
+ [
4670
+ "ąă@",
4671
+ "ĈĀā"
4672
+ ],
4673
+ [
4674
+ "@/đ@*",
4675
+ "Ā"
4676
+ ],
4677
+ [
4678
+ "!",
4679
+ "%"
4680
+ ],
4681
+ [
4682
+ "\"",
4683
+ "\""
4684
+ ],
4685
+ [
4686
+ "$",
4687
+ "$"
4688
+ ],
4689
+ [
4690
+ "(",
4691
+ "#"
4692
+ ],
4693
+ [
4694
+ ",",
4695
+ "Ć"
4696
+ ],
4697
+ [
4698
+ "<",
4699
+ "Ę"
4700
+ ],
4701
+ [
4702
+ "@",
4703
+ "đ"
4704
+ ],
4705
+ [
4706
+ "@",
4707
+ "ğ"
4708
+ ],
4709
+ [
4710
+ "Ā",
4711
+ "Ā?ėĀĀ"
4712
+ ],
4713
+ [
4714
+ "ă",
4715
+ "ĕā"
4716
+ ],
4717
+ [
4718
+ "ć",
4719
+ "."
4720
+ ],
4721
+ [
4722
+ "ć",
4723
+ "Ā?ĕ"
4724
+ ],
4725
+ [
4726
+ "Ĉ",
4727
+ "Ě"
4728
+ ],
4729
+ [
4730
+ "đ",
4731
+ "ď"
4732
+ ],
4733
+ [
4734
+ "đ",
4735
+ "ę"
4736
+ ],
4737
+ [
4738
+ "Ĕ",
4739
+ "Ēā"
4740
+ ],
4741
+ [
4742
+ "ė",
4743
+ "="
4744
+ ],
4745
+ [
4746
+ "Ę",
4747
+ ":"
4748
+ ],
4749
+ [
4750
+ "Ě",
4751
+ "-"
4752
+ ],
4753
+ [
4754
+ "Ě",
4755
+ "Āā"
4756
+ ],
4757
+ [
4758
+ "ě",
4759
+ "!"
4760
+ ],
4761
+ [
4762
+ "ě",
4763
+ "'"
4764
+ ]
4765
+ ]
4766
+ }
4767
+ }
bpe_tokenizer/tokenizer_config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
4
+ },
5
+ "backend": "tokenizers",
6
+ "clean_up_tokenization_spaces": false,
7
+ "model_max_length": 1000000000000000019884624838656,
8
+ "processor_class": "UniversalActionProcessor",
9
+ "tokenizer_class": "TokenizersBackend"
10
+ }
processing_action_tokenizer.py ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ from typing import ClassVar
3
+
4
+ import numpy as np
5
+ from scipy.fft import dct
6
+ from scipy.fft import idct
7
+ from tokenizers import ByteLevelBPETokenizer
8
+ from tokenizers.trainers import BpeTrainer
9
+ from transformers import PreTrainedTokenizerFast
10
+ from transformers.processing_utils import ProcessorMixin
11
+
12
+
13
+ class UniversalActionProcessor(ProcessorMixin):
14
+ attributes: ClassVar[list[str]] = ["bpe_tokenizer"]
15
+ bpe_tokenizer_class: str = "AutoTokenizer"
16
+
17
+ def __init__(
18
+ self,
19
+ bpe_tokenizer: PreTrainedTokenizerFast,
20
+ scale: float = 10,
21
+ vocab_size: int = 1024,
22
+ min_token: int = 0,
23
+ *,
24
+ action_dim: int | None = None,
25
+ time_horizon: int | None = None,
26
+ ):
27
+ self.scale = scale
28
+ self.vocab_size = vocab_size
29
+ self.min_token = min_token
30
+
31
+ # Action horizon and dimension needed during decoding. These can be specified
32
+ # in three ways (in order of priority):
33
+ # 1. passed in as kwargs to decode()
34
+ # 2. in the constructor
35
+ # 3. cached from the last time decode() was called
36
+ self.time_horizon = time_horizon
37
+ self.action_dim = action_dim
38
+ self.called_time_horizon = time_horizon
39
+ self.called_action_dim = action_dim
40
+
41
+ super().__init__(bpe_tokenizer)
42
+
43
+ def __call__(self, action_chunk: np.array) -> np.array:
44
+ assert action_chunk.ndim <= 3, "Only 3 dimensions supported: [batch, timesteps, action_dim]"
45
+ if action_chunk.ndim == 2:
46
+ action_chunk = action_chunk[None, ...]
47
+
48
+ # Cache the time horizon and action dimension for decoding
49
+ self.called_time_horizon = action_chunk.shape[-2]
50
+ self.called_action_dim = action_chunk.shape[-1]
51
+
52
+ dct_coeff = dct(action_chunk, axis=1, norm="ortho")
53
+ dct_coeff = np.around(dct_coeff * self.scale)
54
+ tokens = []
55
+ for elem in dct_coeff:
56
+ token_str = "".join(map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int)))
57
+ tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
58
+ return tokens
59
+
60
+ def decode(
61
+ self,
62
+ tokens: list[list[int]],
63
+ *,
64
+ time_horizon: int | None = None,
65
+ action_dim: int | None = None,
66
+ ) -> np.array:
67
+ self.time_horizon = time_horizon or self.time_horizon or self.called_time_horizon
68
+ self.action_dim = action_dim or self.action_dim or self.called_action_dim
69
+
70
+ # Cache the time horizon and action dimension for the next call
71
+ self.called_time_horizon = self.time_horizon
72
+ self.called_action_dim = self.action_dim
73
+
74
+ assert (
75
+ self.time_horizon is not None and self.action_dim is not None
76
+ ), "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
77
+
78
+ decoded_actions = []
79
+ for token in tokens:
80
+ try:
81
+ decoded_tokens = self.bpe_tokenizer.decode(token)
82
+ decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.min_token
83
+ decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
84
+ assert (
85
+ decoded_dct_coeff.shape
86
+ == (
87
+ self.time_horizon,
88
+ self.action_dim,
89
+ )
90
+ ), f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
91
+ except Exception as e:
92
+ print(f"Error decoding tokens: {e}")
93
+ print(f"Tokens: {token}")
94
+ decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
95
+ decoded_actions.append(idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho"))
96
+ return np.stack(decoded_actions)
97
+
98
+ @classmethod
99
+ def fit(
100
+ cls,
101
+ action_data: list[np.array],
102
+ scale: float = 10,
103
+ vocab_size: int = 1024,
104
+ *,
105
+ time_horizon: int | None = None,
106
+ action_dim: int | None = None,
107
+ ) -> "UniversalActionProcessor":
108
+ # Run DCT over all inputs
109
+ dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
110
+
111
+ # Quantize and find min token
112
+ max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
113
+ min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
114
+ min_vocab_size = max_token - min_token
115
+
116
+ assert (
117
+ min_vocab_size <= vocab_size
118
+ ), f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
119
+ if min_vocab_size + 100 > vocab_size:
120
+ logging.warning(
121
+ f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
122
+ f"size {vocab_size}, consider increasing vocab size"
123
+ )
124
+
125
+ # Make token iterator for BPE training
126
+ def _token_iter():
127
+ for tokens in dct_tokens:
128
+ rounded_tokens = np.around(tokens * scale) - min_token
129
+ rounded_tokens = rounded_tokens.astype(int)
130
+ string = "".join(map(chr, rounded_tokens))
131
+ yield string
132
+
133
+ # Train BPE tokenizer
134
+ bpe = ByteLevelBPETokenizer()
135
+
136
+ # Set up the entire range of possible tokens as the initial alphabet
137
+ alphabet = [chr(i) for i in range(max_token - min_token + 1)]
138
+ trainer = BpeTrainer(
139
+ vocab_size=vocab_size,
140
+ min_frequency=2,
141
+ show_progress=True,
142
+ special_tokens=[],
143
+ initial_alphabet=alphabet,
144
+ max_token_length=10000,
145
+ )
146
+
147
+ # Train the inner tokenizer (don't use ByteLevelBPETokenizer.train_from_iterator()
148
+ # because it doesn't support custom alphabets)
149
+ bpe._tokenizer.train_from_iterator(_token_iter(), trainer=trainer)
150
+
151
+ return cls(
152
+ PreTrainedTokenizerFast(tokenizer_object=bpe, clean_up_tokenization_spaces=False),
153
+ scale=scale,
154
+ vocab_size=vocab_size,
155
+ min_token=min_token,
156
+ time_horizon=time_horizon,
157
+ action_dim=action_dim,
158
+ )
processor_config.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "action_dim": 6,
3
+ "auto_map": {
4
+ "AutoProcessor": "processing_action_tokenizer.UniversalActionProcessor"
5
+ },
6
+ "min_token": -32,
7
+ "processor_class": "UniversalActionProcessor",
8
+ "scale": 10.0,
9
+ "time_horizon": 10,
10
+ "vocab_size": 1024
11
+ }