File size: 72,929 Bytes
4c65bff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union

import yaml

from ..models import auto as auto_module
from ..models.auto.configuration_auto import model_type_to_module_name
from ..utils import is_flax_available, is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


CURRENT_YEAR = date.today().year
TRANSFORMERS_PATH = Path(__file__).parent.parent
REPO_PATH = TRANSFORMERS_PATH.parent.parent


@dataclass
class ModelPatterns:
    """
    Holds the basic information about a new model for the add-new-model-like command.

    Args:
        model_name (`str`): The model name.
        checkpoint (`str`): The checkpoint to use for doc examples.
        model_type (`str`, *optional*):
            The model type, the identifier used internally in the library like `bert` or `xlm-roberta`. Will default to
            `model_name` lowercased with spaces replaced with minuses (-).
        model_lower_cased (`str`, *optional*):
            The lowercased version of the model name, to use for the module name or function names. Will default to
            `model_name` lowercased with spaces and minuses replaced with underscores.
        model_camel_cased (`str`, *optional*):
            The camel-cased version of the model name, to use for the class names. Will default to `model_name`
            camel-cased (with spaces and minuses both considered as word separators.
        model_upper_cased (`str`, *optional*):
            The uppercased version of the model name, to use for the constant names. Will default to `model_name`
            uppercased with spaces and minuses replaced with underscores.
        config_class (`str`, *optional*):
            The tokenizer class associated with this model. Will default to `"{model_camel_cased}Config"`.
        tokenizer_class (`str`, *optional*):
            The tokenizer class associated with this model (leave to `None` for models that don't use a tokenizer).
        image_processor_class (`str`, *optional*):
            The image processor class associated with this model (leave to `None` for models that don't use an image
            processor).
        feature_extractor_class (`str`, *optional*):
            The feature extractor class associated with this model (leave to `None` for models that don't use a feature
            extractor).
        processor_class (`str`, *optional*):
            The processor class associated with this model (leave to `None` for models that don't use a processor).
    """

    model_name: str
    checkpoint: str
    model_type: Optional[str] = None
    model_lower_cased: Optional[str] = None
    model_camel_cased: Optional[str] = None
    model_upper_cased: Optional[str] = None
    config_class: Optional[str] = None
    tokenizer_class: Optional[str] = None
    image_processor_class: Optional[str] = None
    feature_extractor_class: Optional[str] = None
    processor_class: Optional[str] = None

    def __post_init__(self):
        if self.model_type is None:
            self.model_type = self.model_name.lower().replace(" ", "-")
        if self.model_lower_cased is None:
            self.model_lower_cased = self.model_name.lower().replace(" ", "_").replace("-", "_")
        if self.model_camel_cased is None:
            # Split the model name on - and space
            words = self.model_name.split(" ")
            words = list(chain(*[w.split("-") for w in words]))
            # Make sure each word is capitalized
            words = [w[0].upper() + w[1:] for w in words]
            self.model_camel_cased = "".join(words)
        if self.model_upper_cased is None:
            self.model_upper_cased = self.model_name.upper().replace(" ", "_").replace("-", "_")
        if self.config_class is None:
            self.config_class = f"{self.model_camel_cased}Config"


ATTRIBUTE_TO_PLACEHOLDER = {
    "config_class": "[CONFIG_CLASS]",
    "tokenizer_class": "[TOKENIZER_CLASS]",
    "image_processor_class": "[IMAGE_PROCESSOR_CLASS]",
    "feature_extractor_class": "[FEATURE_EXTRACTOR_CLASS]",
    "processor_class": "[PROCESSOR_CLASS]",
    "checkpoint": "[CHECKPOINT]",
    "model_type": "[MODEL_TYPE]",
    "model_upper_cased": "[MODEL_UPPER_CASED]",
    "model_camel_cased": "[MODEL_CAMELCASED]",
    "model_lower_cased": "[MODEL_LOWER_CASED]",
    "model_name": "[MODEL_NAME]",
}


def is_empty_line(line: str) -> bool:
    """
    Determines whether a line is empty or not.
    """
    return len(line) == 0 or line.isspace()


def find_indent(line: str) -> int:
    """
    Returns the number of spaces that start a line indent.
    """
    search = re.search(r"^(\s*)(?:\S|$)", line)
    if search is None:
        return 0
    return len(search.groups()[0])


def parse_module_content(content: str) -> List[str]:
    """
    Parse the content of a module in the list of objects it defines.

    Args:
        content (`str`): The content to parse

    Returns:
        `List[str]`: The list of objects defined in the module.
    """
    objects = []
    current_object = []
    lines = content.split("\n")
    # Doc-styler takes everything between two triple quotes in docstrings, so we need a fake """ here to go with this.
    end_markers = [")", "]", "}", '"""']

    for line in lines:
        # End of an object
        is_valid_object = len(current_object) > 0
        if is_valid_object and len(current_object) == 1:
            is_valid_object = not current_object[0].startswith("# Copied from")
        if not is_empty_line(line) and find_indent(line) == 0 and is_valid_object:
            # Closing parts should be included in current object
            if line in end_markers:
                current_object.append(line)
                objects.append("\n".join(current_object))
                current_object = []
            else:
                objects.append("\n".join(current_object))
                current_object = [line]
        else:
            current_object.append(line)

    # Add last object
    if len(current_object) > 0:
        objects.append("\n".join(current_object))

    return objects


def extract_block(content: str, indent_level: int = 0) -> str:
    """Return the first block in `content` with the indent level `indent_level`.

    The first line in `content` should be indented at `indent_level` level, otherwise an error will be thrown.

    This method will immediately stop the search when a (non-empty) line with indent level less than `indent_level` is
    encountered.

    Args:
        content (`str`): The content to parse
        indent_level (`int`, *optional*, default to 0): The indent level of the blocks to search for

    Returns:
        `str`: The first block in `content` with the indent level `indent_level`.
    """
    current_object = []
    lines = content.split("\n")
    # Doc-styler takes everything between two triple quotes in docstrings, so we need a fake """ here to go with this.
    end_markers = [")", "]", "}", '"""']

    for idx, line in enumerate(lines):
        if idx == 0 and indent_level > 0 and not is_empty_line(line) and find_indent(line) != indent_level:
            raise ValueError(
                f"When `indent_level > 0`, the first line in `content` should have indent level {indent_level}. Got "
                f"{find_indent(line)} instead."
            )

        if find_indent(line) < indent_level and not is_empty_line(line):
            break

        # End of an object
        is_valid_object = len(current_object) > 0
        if (
            not is_empty_line(line)
            and not line.endswith(":")
            and find_indent(line) == indent_level
            and is_valid_object
        ):
            # Closing parts should be included in current object
            if line.lstrip() in end_markers:
                current_object.append(line)
            return "\n".join(current_object)
        else:
            current_object.append(line)

    # Add last object
    if len(current_object) > 0:
        return "\n".join(current_object)


def add_content_to_text(
    text: str,
    content: str,
    add_after: Optional[Union[str, Pattern]] = None,
    add_before: Optional[Union[str, Pattern]] = None,
    exact_match: bool = False,
) -> str:
    """
    A utility to add some content inside a given text.

    Args:
       text (`str`): The text in which we want to insert some content.
       content (`str`): The content to add.
       add_after (`str` or `Pattern`):
           The pattern to test on a line of `text`, the new content is added after the first instance matching it.
       add_before (`str` or `Pattern`):
           The pattern to test on a line of `text`, the new content is added before the first instance matching it.
       exact_match (`bool`, *optional*, defaults to `False`):
           A line is considered a match with `add_after` or `add_before` if it matches exactly when `exact_match=True`,
           otherwise, if `add_after`/`add_before` is present in the line.

    <Tip warning={true}>

    The arguments `add_after` and `add_before` are mutually exclusive, and one exactly needs to be provided.

    </Tip>

    Returns:
        `str`: The text with the new content added if a match was found.
    """
    if add_after is None and add_before is None:
        raise ValueError("You need to pass either `add_after` or `add_before`")
    if add_after is not None and add_before is not None:
        raise ValueError("You can't pass both `add_after` or `add_before`")
    pattern = add_after if add_before is None else add_before

    def this_is_the_line(line):
        if isinstance(pattern, Pattern):
            return pattern.search(line) is not None
        elif exact_match:
            return pattern == line
        else:
            return pattern in line

    new_lines = []
    for line in text.split("\n"):
        if this_is_the_line(line):
            if add_before is not None:
                new_lines.append(content)
            new_lines.append(line)
            if add_after is not None:
                new_lines.append(content)
        else:
            new_lines.append(line)

    return "\n".join(new_lines)


def add_content_to_file(
    file_name: Union[str, os.PathLike],
    content: str,
    add_after: Optional[Union[str, Pattern]] = None,
    add_before: Optional[Union[str, Pattern]] = None,
    exact_match: bool = False,
):
    """
    A utility to add some content inside a given file.

    Args:
       file_name (`str` or `os.PathLike`): The name of the file in which we want to insert some content.
       content (`str`): The content to add.
       add_after (`str` or `Pattern`):
           The pattern to test on a line of `text`, the new content is added after the first instance matching it.
       add_before (`str` or `Pattern`):
           The pattern to test on a line of `text`, the new content is added before the first instance matching it.
       exact_match (`bool`, *optional*, defaults to `False`):
           A line is considered a match with `add_after` or `add_before` if it matches exactly when `exact_match=True`,
           otherwise, if `add_after`/`add_before` is present in the line.

    <Tip warning={true}>

    The arguments `add_after` and `add_before` are mutually exclusive, and one exactly needs to be provided.

    </Tip>
    """
    with open(file_name, "r", encoding="utf-8") as f:
        old_content = f.read()

    new_content = add_content_to_text(
        old_content, content, add_after=add_after, add_before=add_before, exact_match=exact_match
    )

    with open(file_name, "w", encoding="utf-8") as f:
        f.write(new_content)


def replace_model_patterns(
    text: str, old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns
) -> Tuple[str, str]:
    """
    Replace all patterns present in a given text.

    Args:
        text (`str`): The text to treat.
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.

    Returns:
        `Tuple(str, str)`: A tuple of with the treated text and the replacement actually done in it.
    """
    # The order is crucially important as we will check and replace in that order. For instance the config probably
    # contains the camel-cased named, but will be treated before.
    attributes_to_check = ["config_class"]
    # Add relevant preprocessing classes
    for attr in ["tokenizer_class", "image_processor_class", "feature_extractor_class", "processor_class"]:
        if getattr(old_model_patterns, attr) is not None and getattr(new_model_patterns, attr) is not None:
            attributes_to_check.append(attr)

    # Special cases for checkpoint and model_type
    if old_model_patterns.checkpoint not in [old_model_patterns.model_type, old_model_patterns.model_lower_cased]:
        attributes_to_check.append("checkpoint")
    if old_model_patterns.model_type != old_model_patterns.model_lower_cased:
        attributes_to_check.append("model_type")
    else:
        text = re.sub(
            rf'(\s*)model_type = "{old_model_patterns.model_type}"',
            r'\1model_type = "[MODEL_TYPE]"',
            text,
        )

    # Special case when the model camel cased and upper cased names are the same for the old model (like for GPT2) but
    # not the new one. We can't just do a replace in all the text and will need a special regex
    if old_model_patterns.model_upper_cased == old_model_patterns.model_camel_cased:
        old_model_value = old_model_patterns.model_upper_cased
        if re.search(rf"{old_model_value}_[A-Z_]*[^A-Z_]", text) is not None:
            text = re.sub(rf"{old_model_value}([A-Z_]*)([^a-zA-Z_])", r"[MODEL_UPPER_CASED]\1\2", text)
    else:
        attributes_to_check.append("model_upper_cased")

    attributes_to_check.extend(["model_camel_cased", "model_lower_cased", "model_name"])

    # Now let's replace every other attribute by their placeholder
    for attr in attributes_to_check:
        text = text.replace(getattr(old_model_patterns, attr), ATTRIBUTE_TO_PLACEHOLDER[attr])

    # Finally we can replace the placeholder byt the new values.
    replacements = []
    for attr, placeholder in ATTRIBUTE_TO_PLACEHOLDER.items():
        if placeholder in text:
            replacements.append((getattr(old_model_patterns, attr), getattr(new_model_patterns, attr)))
            text = text.replace(placeholder, getattr(new_model_patterns, attr))

    # If we have two inconsistent replacements, we don't return anything (ex: GPT2->GPT_NEW and GPT2->GPTNew)
    old_replacement_values = [old for old, new in replacements]
    if len(set(old_replacement_values)) != len(old_replacement_values):
        return text, ""

    replacements = simplify_replacements(replacements)
    replacements = [f"{old}->{new}" for old, new in replacements]
    return text, ",".join(replacements)


def simplify_replacements(replacements):
    """
    Simplify a list of replacement patterns to make sure there are no needless ones.

    For instance in the sequence "Bert->BertNew, BertConfig->BertNewConfig, bert->bert_new", the replacement
    "BertConfig->BertNewConfig" is implied by "Bert->BertNew" so not needed.

    Args:
        replacements (`List[Tuple[str, str]]`): List of patterns (old, new)

    Returns:
        `List[Tuple[str, str]]`: The list of patterns simplified.
    """
    if len(replacements) <= 1:
        # Nothing to simplify
        return replacements

    # Next let's sort replacements by length as a replacement can only "imply" another replacement if it's shorter.
    replacements.sort(key=lambda x: len(x[0]))

    idx = 0
    while idx < len(replacements):
        old, new = replacements[idx]
        # Loop through all replacements after
        j = idx + 1
        while j < len(replacements):
            old_2, new_2 = replacements[j]
            # If the replacement is implied by the current one, we can drop it.
            if old_2.replace(old, new) == new_2:
                replacements.pop(j)
            else:
                j += 1
        idx += 1

    return replacements


def get_module_from_file(module_file: Union[str, os.PathLike]) -> str:
    """
    Returns the module name corresponding to a module file.
    """
    full_module_path = Path(module_file).absolute()
    module_parts = full_module_path.with_suffix("").parts

    # Find the first part named transformers, starting from the end.
    idx = len(module_parts) - 1
    while idx >= 0 and module_parts[idx] != "transformers":
        idx -= 1
    if idx < 0:
        raise ValueError(f"{module_file} is not a transformers module.")

    return ".".join(module_parts[idx:])


SPECIAL_PATTERNS = {
    "_CHECKPOINT_FOR_DOC =": "checkpoint",
    "_CONFIG_FOR_DOC =": "config_class",
    "_TOKENIZER_FOR_DOC =": "tokenizer_class",
    "_IMAGE_PROCESSOR_FOR_DOC =": "image_processor_class",
    "_FEAT_EXTRACTOR_FOR_DOC =": "feature_extractor_class",
    "_PROCESSOR_FOR_DOC =": "processor_class",
}


_re_class_func = re.compile(r"^(?:class|def)\s+([^\s:\(]+)\s*(?:\(|\:)", flags=re.MULTILINE)


def remove_attributes(obj, target_attr):
    """Remove `target_attr` in `obj`."""
    lines = obj.split(os.linesep)

    target_idx = None
    for idx, line in enumerate(lines):
        # search for assignment
        if line.lstrip().startswith(f"{target_attr} = "):
            target_idx = idx
            break
        # search for function/method definition
        elif line.lstrip().startswith(f"def {target_attr}("):
            target_idx = idx
            break

    # target not found
    if target_idx is None:
        return obj

    line = lines[target_idx]
    indent_level = find_indent(line)
    # forward pass to find the ending of the block (including empty lines)
    parsed = extract_block("\n".join(lines[target_idx:]), indent_level)
    num_lines = len(parsed.split("\n"))
    for idx in range(num_lines):
        lines[target_idx + idx] = None

    # backward pass to find comments or decorator
    for idx in range(target_idx - 1, -1, -1):
        line = lines[idx]
        if (line.lstrip().startswith("#") or line.lstrip().startswith("@")) and find_indent(line) == indent_level:
            lines[idx] = None
        else:
            break

    new_obj = os.linesep.join([x for x in lines if x is not None])

    return new_obj


def duplicate_module(
    module_file: Union[str, os.PathLike],
    old_model_patterns: ModelPatterns,
    new_model_patterns: ModelPatterns,
    dest_file: Optional[str] = None,
    add_copied_from: bool = True,
    attrs_to_remove: List[str] = None,
):
    """
    Create a new module from an existing one and adapting all function and classes names from old patterns to new ones.

    Args:
        module_file (`str` or `os.PathLike`): Path to the module to duplicate.
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
        dest_file (`str` or `os.PathLike`, *optional*): Path to the new module.
        add_copied_from (`bool`, *optional*, defaults to `True`):
            Whether or not to add `# Copied from` statements in the duplicated module.
    """
    if dest_file is None:
        dest_file = str(module_file).replace(
            old_model_patterns.model_lower_cased, new_model_patterns.model_lower_cased
        )

    with open(module_file, "r", encoding="utf-8") as f:
        content = f.read()

    content = re.sub(r"# Copyright (\d+)\s", f"# Copyright {CURRENT_YEAR} ", content)
    objects = parse_module_content(content)

    # Loop and treat all objects
    new_objects = []
    for obj in objects:
        # Special cases
        if "PRETRAINED_CONFIG_ARCHIVE_MAP = {" in obj:
            # docstyle-ignore
            obj = (
                f"{new_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP = "
                + "{"
                + f"""
    "{new_model_patterns.checkpoint}": "https://huggingface.co/{new_model_patterns.checkpoint}/resolve/main/config.json",
"""
                + "}\n"
            )
            new_objects.append(obj)
            continue
        elif "PRETRAINED_MODEL_ARCHIVE_LIST = [" in obj:
            if obj.startswith("TF_"):
                prefix = "TF_"
            elif obj.startswith("FLAX_"):
                prefix = "FLAX_"
            else:
                prefix = ""
            # docstyle-ignore
            obj = f"""{prefix}{new_model_patterns.model_upper_cased}_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "{new_model_patterns.checkpoint}",
    # See all {new_model_patterns.model_name} models at https://huggingface.co/models?filter={new_model_patterns.model_type}
]
"""
            new_objects.append(obj)
            continue

        special_pattern = False
        for pattern, attr in SPECIAL_PATTERNS.items():
            if pattern in obj:
                obj = obj.replace(getattr(old_model_patterns, attr), getattr(new_model_patterns, attr))
                new_objects.append(obj)
                special_pattern = True
                break

        if special_pattern:
            continue

        # Regular classes functions
        old_obj = obj
        obj, replacement = replace_model_patterns(obj, old_model_patterns, new_model_patterns)
        has_copied_from = re.search(r"^#\s+Copied from", obj, flags=re.MULTILINE) is not None
        if add_copied_from and not has_copied_from and _re_class_func.search(obj) is not None and len(replacement) > 0:
            # Copied from statement must be added just before the class/function definition, which may not be the
            # first line because of decorators.
            module_name = get_module_from_file(module_file)
            old_object_name = _re_class_func.search(old_obj).groups()[0]
            obj = add_content_to_text(
                obj, f"# Copied from {module_name}.{old_object_name} with {replacement}", add_before=_re_class_func
            )
        # In all cases, we remove Copied from statement with indent on methods.
        obj = re.sub("\n[ ]+# Copied from [^\n]*\n", "\n", obj)

        new_objects.append(obj)

    content = "\n".join(new_objects)
    # Remove some attributes that we don't want to copy to the new file(s)
    if attrs_to_remove is not None:
        for attr in attrs_to_remove:
            content = remove_attributes(content, target_attr=attr)

    with open(dest_file, "w", encoding="utf-8") as f:
        f.write(content)


def filter_framework_files(
    files: List[Union[str, os.PathLike]], frameworks: Optional[List[str]] = None
) -> List[Union[str, os.PathLike]]:
    """
    Filter a list of files to only keep the ones corresponding to a list of frameworks.

    Args:
        files (`List[Union[str, os.PathLike]]`): The list of files to filter.
        frameworks (`List[str]`, *optional*): The list of allowed frameworks.

    Returns:
        `List[Union[str, os.PathLike]]`: The list of filtered files.
    """
    if frameworks is None:
        frameworks = get_default_frameworks()

    framework_to_file = {}
    others = []
    for f in files:
        parts = Path(f).name.split("_")
        if "modeling" not in parts:
            others.append(f)
            continue
        if "tf" in parts:
            framework_to_file["tf"] = f
        elif "flax" in parts:
            framework_to_file["flax"] = f
        else:
            framework_to_file["pt"] = f

    return [framework_to_file[f] for f in frameworks if f in framework_to_file] + others


def get_model_files(model_type: str, frameworks: Optional[List[str]] = None) -> Dict[str, Union[Path, List[Path]]]:
    """
    Retrieves all the files associated to a model.

    Args:
        model_type (`str`): A valid model type (like "bert" or "gpt2")
        frameworks (`List[str]`, *optional*):
            If passed, will only keep the model files corresponding to the passed frameworks.

    Returns:
        `Dict[str, Union[Path, List[Path]]]`: A dictionary with the following keys:
        - **doc_file** -- The documentation file for the model.
        - **model_files** -- All the files in the model module.
        - **test_files** -- The test files for the model.
    """
    module_name = model_type_to_module_name(model_type)

    model_module = TRANSFORMERS_PATH / "models" / module_name
    model_files = list(model_module.glob("*.py"))
    model_files = filter_framework_files(model_files, frameworks=frameworks)

    doc_file = REPO_PATH / "docs" / "source" / "en" / "model_doc" / f"{model_type}.md"

    # Basic pattern for test files
    test_files = [
        f"test_modeling_{module_name}.py",
        f"test_modeling_tf_{module_name}.py",
        f"test_modeling_flax_{module_name}.py",
        f"test_tokenization_{module_name}.py",
        f"test_image_processing_{module_name}.py",
        f"test_feature_extraction_{module_name}.py",
        f"test_processor_{module_name}.py",
    ]
    test_files = filter_framework_files(test_files, frameworks=frameworks)
    # Add the test directory
    test_files = [REPO_PATH / "tests" / "models" / module_name / f for f in test_files]
    # Filter by existing files
    test_files = [f for f in test_files if f.exists()]

    return {"doc_file": doc_file, "model_files": model_files, "module_name": module_name, "test_files": test_files}


_re_checkpoint_for_doc = re.compile(r"^_CHECKPOINT_FOR_DOC\s+=\s+(\S*)\s*$", flags=re.MULTILINE)


def find_base_model_checkpoint(
    model_type: str, model_files: Optional[Dict[str, Union[Path, List[Path]]]] = None
) -> str:
    """
    Finds the model checkpoint used in the docstrings for a given model.

    Args:
        model_type (`str`): A valid model type (like "bert" or "gpt2")
        model_files (`Dict[str, Union[Path, List[Path]]`, *optional*):
            The files associated to `model_type`. Can be passed to speed up the function, otherwise will be computed.

    Returns:
        `str`: The checkpoint used.
    """
    if model_files is None:
        model_files = get_model_files(model_type)
    module_files = model_files["model_files"]
    for fname in module_files:
        if "modeling" not in str(fname):
            continue

        with open(fname, "r", encoding="utf-8") as f:
            content = f.read()
            if _re_checkpoint_for_doc.search(content) is not None:
                checkpoint = _re_checkpoint_for_doc.search(content).groups()[0]
                # Remove quotes
                checkpoint = checkpoint.replace('"', "")
                checkpoint = checkpoint.replace("'", "")
                return checkpoint

    # TODO: Find some kind of fallback if there is no _CHECKPOINT_FOR_DOC in any of the modeling file.
    return ""


def get_default_frameworks():
    """
    Returns the list of frameworks (PyTorch, TensorFlow, Flax) that are installed in the environment.
    """
    frameworks = []
    if is_torch_available():
        frameworks.append("pt")
    if is_tf_available():
        frameworks.append("tf")
    if is_flax_available():
        frameworks.append("flax")
    return frameworks


_re_model_mapping = re.compile("MODEL_([A-Z_]*)MAPPING_NAMES")


def retrieve_model_classes(model_type: str, frameworks: Optional[List[str]] = None) -> Dict[str, List[str]]:
    """
    Retrieve the model classes associated to a given model.

    Args:
        model_type (`str`): A valid model type (like "bert" or "gpt2")
        frameworks (`List[str]`, *optional*):
            The frameworks to look for. Will default to `["pt", "tf", "flax"]`, passing a smaller list will restrict
            the classes returned.

    Returns:
        `Dict[str, List[str]]`: A dictionary with one key per framework and the list of model classes associated to
        that framework as values.
    """
    if frameworks is None:
        frameworks = get_default_frameworks()

    modules = {
        "pt": auto_module.modeling_auto if is_torch_available() else None,
        "tf": auto_module.modeling_tf_auto if is_tf_available() else None,
        "flax": auto_module.modeling_flax_auto if is_flax_available() else None,
    }

    model_classes = {}
    for framework in frameworks:
        new_model_classes = []
        if modules[framework] is None:
            raise ValueError(f"You selected {framework} in the frameworks, but it is not installed.")
        model_mappings = [attr for attr in dir(modules[framework]) if _re_model_mapping.search(attr) is not None]
        for model_mapping_name in model_mappings:
            model_mapping = getattr(modules[framework], model_mapping_name)
            if model_type in model_mapping:
                new_model_classes.append(model_mapping[model_type])

        if len(new_model_classes) > 0:
            # Remove duplicates
            model_classes[framework] = list(set(new_model_classes))

    return model_classes


def retrieve_info_for_model(model_type, frameworks: Optional[List[str]] = None):
    """
    Retrieves all the information from a given model_type.

    Args:
        model_type (`str`): A valid model type (like "bert" or "gpt2")
        frameworks (`List[str]`, *optional*):
            If passed, will only keep the info corresponding to the passed frameworks.

    Returns:
        `Dict`: A dictionary with the following keys:
        - **frameworks** (`List[str]`): The list of frameworks that back this model type.
        - **model_classes** (`Dict[str, List[str]]`): The model classes implemented for that model type.
        - **model_files** (`Dict[str, Union[Path, List[Path]]]`): The files associated with that model type.
        - **model_patterns** (`ModelPatterns`): The various patterns for the model.
    """
    if model_type not in auto_module.MODEL_NAMES_MAPPING:
        raise ValueError(f"{model_type} is not a valid model type.")

    model_name = auto_module.MODEL_NAMES_MAPPING[model_type]
    config_class = auto_module.configuration_auto.CONFIG_MAPPING_NAMES[model_type]
    archive_map = auto_module.configuration_auto.CONFIG_ARCHIVE_MAP_MAPPING_NAMES.get(model_type, None)
    if model_type in auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES:
        tokenizer_classes = auto_module.tokenization_auto.TOKENIZER_MAPPING_NAMES[model_type]
        tokenizer_class = tokenizer_classes[0] if tokenizer_classes[0] is not None else tokenizer_classes[1]
    else:
        tokenizer_class = None
    image_processor_class = auto_module.image_processing_auto.IMAGE_PROCESSOR_MAPPING_NAMES.get(model_type, None)
    feature_extractor_class = auto_module.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES.get(model_type, None)
    processor_class = auto_module.processing_auto.PROCESSOR_MAPPING_NAMES.get(model_type, None)

    model_files = get_model_files(model_type, frameworks=frameworks)
    model_camel_cased = config_class.replace("Config", "")

    available_frameworks = []
    for fname in model_files["model_files"]:
        if "modeling_tf" in str(fname):
            available_frameworks.append("tf")
        elif "modeling_flax" in str(fname):
            available_frameworks.append("flax")
        elif "modeling" in str(fname):
            available_frameworks.append("pt")

    if frameworks is None:
        frameworks = get_default_frameworks()

    frameworks = [f for f in frameworks if f in available_frameworks]

    model_classes = retrieve_model_classes(model_type, frameworks=frameworks)

    # Retrieve model upper-cased name from the constant name of the pretrained archive map.
    if archive_map is None:
        model_upper_cased = model_camel_cased.upper()
    else:
        parts = archive_map.split("_")
        idx = 0
        while idx < len(parts) and parts[idx] != "PRETRAINED":
            idx += 1
        if idx < len(parts):
            model_upper_cased = "_".join(parts[:idx])
        else:
            model_upper_cased = model_camel_cased.upper()

    model_patterns = ModelPatterns(
        model_name,
        checkpoint=find_base_model_checkpoint(model_type, model_files=model_files),
        model_type=model_type,
        model_camel_cased=model_camel_cased,
        model_lower_cased=model_files["module_name"],
        model_upper_cased=model_upper_cased,
        config_class=config_class,
        tokenizer_class=tokenizer_class,
        image_processor_class=image_processor_class,
        feature_extractor_class=feature_extractor_class,
        processor_class=processor_class,
    )

    return {
        "frameworks": frameworks,
        "model_classes": model_classes,
        "model_files": model_files,
        "model_patterns": model_patterns,
    }


def clean_frameworks_in_init(
    init_file: Union[str, os.PathLike], frameworks: Optional[List[str]] = None, keep_processing: bool = True
):
    """
    Removes all the import lines that don't belong to a given list of frameworks or concern tokenizers/feature
    extractors/image processors/processors in an init.

    Args:
        init_file (`str` or `os.PathLike`): The path to the init to treat.
        frameworks (`List[str]`, *optional*):
           If passed, this will remove all imports that are subject to a framework not in frameworks
        keep_processing (`bool`, *optional*, defaults to `True`):
            Whether or not to keep the preprocessing (tokenizer, feature extractor, image processor, processor) imports
            in the init.
    """
    if frameworks is None:
        frameworks = get_default_frameworks()

    names = {"pt": "torch"}
    to_remove = [names.get(f, f) for f in ["pt", "tf", "flax"] if f not in frameworks]
    if not keep_processing:
        to_remove.extend(["sentencepiece", "tokenizers", "vision"])

    if len(to_remove) == 0:
        # Nothing to do
        return

    remove_pattern = "|".join(to_remove)
    re_conditional_imports = re.compile(rf"^\s*if not is_({remove_pattern})_available\(\):\s*$")
    re_try = re.compile(r"\s*try:")
    re_else = re.compile(r"\s*else:")
    re_is_xxx_available = re.compile(rf"is_({remove_pattern})_available")

    with open(init_file, "r", encoding="utf-8") as f:
        content = f.read()

    lines = content.split("\n")
    new_lines = []
    idx = 0
    while idx < len(lines):
        # Conditional imports in try-except-else blocks
        if (re_conditional_imports.search(lines[idx]) is not None) and (re_try.search(lines[idx - 1]) is not None):
            # Remove the preceding `try:`
            new_lines.pop()
            idx += 1
            # Iterate until `else:`
            while is_empty_line(lines[idx]) or re_else.search(lines[idx]) is None:
                idx += 1
            idx += 1
            indent = find_indent(lines[idx])
            while find_indent(lines[idx]) >= indent or is_empty_line(lines[idx]):
                idx += 1
        # Remove the import from utils
        elif re_is_xxx_available.search(lines[idx]) is not None:
            line = lines[idx]
            for framework in to_remove:
                line = line.replace(f", is_{framework}_available", "")
                line = line.replace(f"is_{framework}_available, ", "")
                line = line.replace(f"is_{framework}_available,", "")
                line = line.replace(f"is_{framework}_available", "")

            if len(line.strip()) > 0:
                new_lines.append(line)
            idx += 1
        # Otherwise we keep the line, except if it's a tokenizer import and we don't want to keep it.
        elif keep_processing or (
            re.search(r'^\s*"(tokenization|processing|feature_extraction|image_processing)', lines[idx]) is None
            and re.search(r"^\s*from .(tokenization|processing|feature_extraction|image_processing)", lines[idx])
            is None
        ):
            new_lines.append(lines[idx])
            idx += 1
        else:
            idx += 1

    with open(init_file, "w", encoding="utf-8") as f:
        f.write("\n".join(new_lines))


def add_model_to_main_init(
    old_model_patterns: ModelPatterns,
    new_model_patterns: ModelPatterns,
    frameworks: Optional[List[str]] = None,
    with_processing: bool = True,
):
    """
    Add a model to the main init of Transformers.

    Args:
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
        frameworks (`List[str]`, *optional*):
            If specified, only the models implemented in those frameworks will be added.
        with_processsing (`bool`, *optional*, defaults to `True`):
            Whether the tokenizer/feature extractor/processor of the model should also be added to the init or not.
    """
    with open(TRANSFORMERS_PATH / "__init__.py", "r", encoding="utf-8") as f:
        content = f.read()

    lines = content.split("\n")
    idx = 0
    new_lines = []
    framework = None
    while idx < len(lines):
        new_framework = False
        if not is_empty_line(lines[idx]) and find_indent(lines[idx]) == 0:
            framework = None
        elif lines[idx].lstrip().startswith("if not is_torch_available"):
            framework = "pt"
            new_framework = True
        elif lines[idx].lstrip().startswith("if not is_tf_available"):
            framework = "tf"
            new_framework = True
        elif lines[idx].lstrip().startswith("if not is_flax_available"):
            framework = "flax"
            new_framework = True

        if new_framework:
            # For a new framework, we need to skip until the else: block to get where the imports are.
            while lines[idx].strip() != "else:":
                new_lines.append(lines[idx])
                idx += 1

        # Skip if we are in a framework not wanted.
        if framework is not None and frameworks is not None and framework not in frameworks:
            new_lines.append(lines[idx])
            idx += 1
        elif re.search(rf'models.{old_model_patterns.model_lower_cased}( |")', lines[idx]) is not None:
            block = [lines[idx]]
            indent = find_indent(lines[idx])
            idx += 1
            while find_indent(lines[idx]) > indent:
                block.append(lines[idx])
                idx += 1
            if lines[idx].strip() in [")", "]", "],"]:
                block.append(lines[idx])
                idx += 1
            block = "\n".join(block)
            new_lines.append(block)

            add_block = True
            if not with_processing:
                processing_classes = [
                    old_model_patterns.tokenizer_class,
                    old_model_patterns.image_processor_class,
                    old_model_patterns.feature_extractor_class,
                    old_model_patterns.processor_class,
                ]
                # Only keep the ones that are not None
                processing_classes = [c for c in processing_classes if c is not None]
                for processing_class in processing_classes:
                    block = block.replace(f' "{processing_class}",', "")
                    block = block.replace(f', "{processing_class}"', "")
                    block = block.replace(f" {processing_class},", "")
                    block = block.replace(f", {processing_class}", "")

                    if processing_class in block:
                        add_block = False
            if add_block:
                new_lines.append(replace_model_patterns(block, old_model_patterns, new_model_patterns)[0])
        else:
            new_lines.append(lines[idx])
            idx += 1

    with open(TRANSFORMERS_PATH / "__init__.py", "w", encoding="utf-8") as f:
        f.write("\n".join(new_lines))


def insert_tokenizer_in_auto_module(old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns):
    """
    Add a tokenizer to the relevant mappings in the auto module.

    Args:
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
    """
    if old_model_patterns.tokenizer_class is None or new_model_patterns.tokenizer_class is None:
        return

    with open(TRANSFORMERS_PATH / "models" / "auto" / "tokenization_auto.py", "r", encoding="utf-8") as f:
        content = f.read()

    lines = content.split("\n")
    idx = 0
    # First we get to the TOKENIZER_MAPPING_NAMES block.
    while not lines[idx].startswith("    TOKENIZER_MAPPING_NAMES = OrderedDict("):
        idx += 1
    idx += 1

    # That block will end at this prompt:
    while not lines[idx].startswith("TOKENIZER_MAPPING = _LazyAutoMapping"):
        # Either all the tokenizer block is defined on one line, in which case, it ends with "),"
        if lines[idx].endswith(","):
            block = lines[idx]
        # Otherwise it takes several lines until we get to a "),"
        else:
            block = []
            while not lines[idx].startswith("            ),"):
                block.append(lines[idx])
                idx += 1
            block = "\n".join(block)
        idx += 1

        # If we find the model type and tokenizer class in that block, we have the old model tokenizer block
        if f'"{old_model_patterns.model_type}"' in block and old_model_patterns.tokenizer_class in block:
            break

    new_block = block.replace(old_model_patterns.model_type, new_model_patterns.model_type)
    new_block = new_block.replace(old_model_patterns.tokenizer_class, new_model_patterns.tokenizer_class)

    new_lines = lines[:idx] + [new_block] + lines[idx:]
    with open(TRANSFORMERS_PATH / "models" / "auto" / "tokenization_auto.py", "w", encoding="utf-8") as f:
        f.write("\n".join(new_lines))


AUTO_CLASSES_PATTERNS = {
    "configuration_auto.py": [
        '        ("{model_type}", "{model_name}"),',
        '        ("{model_type}", "{config_class}"),',
        '        ("{model_type}", "{pretrained_archive_map}"),',
    ],
    "feature_extraction_auto.py": ['        ("{model_type}", "{feature_extractor_class}"),'],
    "image_processing_auto.py": ['        ("{model_type}", "{image_processor_class}"),'],
    "modeling_auto.py": ['        ("{model_type}", "{any_pt_class}"),'],
    "modeling_tf_auto.py": ['        ("{model_type}", "{any_tf_class}"),'],
    "modeling_flax_auto.py": ['        ("{model_type}", "{any_flax_class}"),'],
    "processing_auto.py": ['        ("{model_type}", "{processor_class}"),'],
}


def add_model_to_auto_classes(
    old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns, model_classes: Dict[str, List[str]]
):
    """
    Add a model to the relevant mappings in the auto module.

    Args:
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
        model_classes (`Dict[str, List[str]]`): A dictionary framework to list of model classes implemented.
    """
    for filename in AUTO_CLASSES_PATTERNS:
        # Extend patterns with all model classes if necessary
        new_patterns = []
        for pattern in AUTO_CLASSES_PATTERNS[filename]:
            if re.search("any_([a-z]*)_class", pattern) is not None:
                framework = re.search("any_([a-z]*)_class", pattern).groups()[0]
                if framework in model_classes:
                    new_patterns.extend(
                        [
                            pattern.replace("{" + f"any_{framework}_class" + "}", cls)
                            for cls in model_classes[framework]
                        ]
                    )
            elif "{config_class}" in pattern:
                new_patterns.append(pattern.replace("{config_class}", old_model_patterns.config_class))
            elif "{image_processor_class}" in pattern:
                if (
                    old_model_patterns.image_processor_class is not None
                    and new_model_patterns.image_processor_class is not None
                ):
                    new_patterns.append(
                        pattern.replace("{image_processor_class}", old_model_patterns.image_processor_class)
                    )
            elif "{feature_extractor_class}" in pattern:
                if (
                    old_model_patterns.feature_extractor_class is not None
                    and new_model_patterns.feature_extractor_class is not None
                ):
                    new_patterns.append(
                        pattern.replace("{feature_extractor_class}", old_model_patterns.feature_extractor_class)
                    )
            elif "{processor_class}" in pattern:
                if old_model_patterns.processor_class is not None and new_model_patterns.processor_class is not None:
                    new_patterns.append(pattern.replace("{processor_class}", old_model_patterns.processor_class))
            else:
                new_patterns.append(pattern)

        # Loop through all patterns.
        for pattern in new_patterns:
            full_name = TRANSFORMERS_PATH / "models" / "auto" / filename
            old_model_line = pattern
            new_model_line = pattern
            for attr in ["model_type", "model_name"]:
                old_model_line = old_model_line.replace("{" + attr + "}", getattr(old_model_patterns, attr))
                new_model_line = new_model_line.replace("{" + attr + "}", getattr(new_model_patterns, attr))
            if "pretrained_archive_map" in pattern:
                old_model_line = old_model_line.replace(
                    "{pretrained_archive_map}", f"{old_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP"
                )
                new_model_line = new_model_line.replace(
                    "{pretrained_archive_map}", f"{new_model_patterns.model_upper_cased}_PRETRAINED_CONFIG_ARCHIVE_MAP"
                )

            new_model_line = new_model_line.replace(
                old_model_patterns.model_camel_cased, new_model_patterns.model_camel_cased
            )

            add_content_to_file(full_name, new_model_line, add_after=old_model_line)

    # Tokenizers require special handling
    insert_tokenizer_in_auto_module(old_model_patterns, new_model_patterns)


DOC_OVERVIEW_TEMPLATE = """## Overview

The {model_name} model was proposed in [<INSERT PAPER NAME HERE>](<INSERT PAPER LINK HERE>) by <INSERT AUTHORS HERE>.
<INSERT SHORT SUMMARY HERE>

The abstract from the paper is the following:

*<INSERT PAPER ABSTRACT HERE>*

Tips:

<INSERT TIPS ABOUT MODEL HERE>

This model was contributed by [INSERT YOUR HF USERNAME HERE](https://huggingface.co/<INSERT YOUR HF USERNAME HERE>).
The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>).

"""


def duplicate_doc_file(
    doc_file: Union[str, os.PathLike],
    old_model_patterns: ModelPatterns,
    new_model_patterns: ModelPatterns,
    dest_file: Optional[Union[str, os.PathLike]] = None,
    frameworks: Optional[List[str]] = None,
):
    """
    Duplicate a documentation file and adapts it for a new model.

    Args:
        module_file (`str` or `os.PathLike`): Path to the doc file to duplicate.
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
        dest_file (`str` or `os.PathLike`, *optional*): Path to the new doc file.
            Will default to the a file named `{new_model_patterns.model_type}.md` in the same folder as `module_file`.
        frameworks (`List[str]`, *optional*):
            If passed, will only keep the model classes corresponding to this list of frameworks in the new doc file.
    """
    with open(doc_file, "r", encoding="utf-8") as f:
        content = f.read()

    content = re.sub(r"<!--\s*Copyright (\d+)\s", f"<!--Copyright {CURRENT_YEAR} ", content)
    if frameworks is None:
        frameworks = get_default_frameworks()
    if dest_file is None:
        dest_file = Path(doc_file).parent / f"{new_model_patterns.model_type}.md"

    # Parse the doc file in blocks. One block per section/header
    lines = content.split("\n")
    blocks = []
    current_block = []

    for line in lines:
        if line.startswith("#"):
            blocks.append("\n".join(current_block))
            current_block = [line]
        else:
            current_block.append(line)
    blocks.append("\n".join(current_block))

    new_blocks = []
    in_classes = False
    for block in blocks:
        # Copyright
        if not block.startswith("#"):
            new_blocks.append(block)
        # Main title
        elif re.search(r"^#\s+\S+", block) is not None:
            new_blocks.append(f"# {new_model_patterns.model_name}\n")
        # The config starts the part of the doc with the classes.
        elif not in_classes and old_model_patterns.config_class in block.split("\n")[0]:
            in_classes = True
            new_blocks.append(DOC_OVERVIEW_TEMPLATE.format(model_name=new_model_patterns.model_name))
            new_block, _ = replace_model_patterns(block, old_model_patterns, new_model_patterns)
            new_blocks.append(new_block)
        # In classes
        elif in_classes:
            in_classes = True
            block_title = block.split("\n")[0]
            block_class = re.search(r"^#+\s+(\S.*)$", block_title).groups()[0]
            new_block, _ = replace_model_patterns(block, old_model_patterns, new_model_patterns)

            if "Tokenizer" in block_class:
                # We only add the tokenizer if necessary
                if old_model_patterns.tokenizer_class != new_model_patterns.tokenizer_class:
                    new_blocks.append(new_block)
            elif "ImageProcessor" in block_class:
                # We only add the image processor if necessary
                if old_model_patterns.image_processor_class != new_model_patterns.image_processor_class:
                    new_blocks.append(new_block)
            elif "FeatureExtractor" in block_class:
                # We only add the feature extractor if necessary
                if old_model_patterns.feature_extractor_class != new_model_patterns.feature_extractor_class:
                    new_blocks.append(new_block)
            elif "Processor" in block_class:
                # We only add the processor if necessary
                if old_model_patterns.processor_class != new_model_patterns.processor_class:
                    new_blocks.append(new_block)
            elif block_class.startswith("Flax"):
                # We only add Flax models if in the selected frameworks
                if "flax" in frameworks:
                    new_blocks.append(new_block)
            elif block_class.startswith("TF"):
                # We only add TF models if in the selected frameworks
                if "tf" in frameworks:
                    new_blocks.append(new_block)
            elif len(block_class.split(" ")) == 1:
                # We only add PyTorch models if in the selected frameworks
                if "pt" in frameworks:
                    new_blocks.append(new_block)
            else:
                new_blocks.append(new_block)

    with open(dest_file, "w", encoding="utf-8") as f:
        f.write("\n".join(new_blocks))


def insert_model_in_doc_toc(old_model_patterns, new_model_patterns):
    """
    Insert the new model in the doc TOC, in the same section as the old model.

    Args:
        old_model_patterns (`ModelPatterns`): The patterns for the old model.
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
    """
    toc_file = REPO_PATH / "docs" / "source" / "en" / "_toctree.yml"
    with open(toc_file, "r", encoding="utf8") as f:
        content = yaml.safe_load(f)

    # Get to the model API doc
    api_idx = 0
    while content[api_idx]["title"] != "API":
        api_idx += 1
    api_doc = content[api_idx]["sections"]

    model_idx = 0
    while api_doc[model_idx]["title"] != "Models":
        model_idx += 1
    model_doc = api_doc[model_idx]["sections"]

    # Find the base model in the Toc
    old_model_type = old_model_patterns.model_type
    section_idx = 0
    while section_idx < len(model_doc):
        sections = [entry["local"] for entry in model_doc[section_idx]["sections"]]
        if f"model_doc/{old_model_type}" in sections:
            break

        section_idx += 1

    if section_idx == len(model_doc):
        old_model = old_model_patterns.model_name
        new_model = new_model_patterns.model_name
        print(f"Did not find {old_model} in the table of content, so you will need to add {new_model} manually.")
        return

    # Add the new model in the same toc
    toc_entry = {"local": f"model_doc/{new_model_patterns.model_type}", "title": new_model_patterns.model_name}
    model_doc[section_idx]["sections"].append(toc_entry)
    model_doc[section_idx]["sections"] = sorted(model_doc[section_idx]["sections"], key=lambda s: s["title"].lower())
    api_doc[model_idx]["sections"] = model_doc
    content[api_idx]["sections"] = api_doc

    with open(toc_file, "w", encoding="utf-8") as f:
        f.write(yaml.dump(content, allow_unicode=True))


def create_new_model_like(
    model_type: str,
    new_model_patterns: ModelPatterns,
    add_copied_from: bool = True,
    frameworks: Optional[List[str]] = None,
    old_checkpoint: Optional[str] = None,
):
    """
    Creates a new model module like a given model of the Transformers library.

    Args:
        model_type (`str`): The model type to duplicate (like "bert" or "gpt2")
        new_model_patterns (`ModelPatterns`): The patterns for the new model.
        add_copied_from (`bool`, *optional*, defaults to `True`):
            Whether or not to add "Copied from" statements to all classes in the new model modeling files.
        frameworks (`List[str]`, *optional*):
            If passed, will limit the duplicate to the frameworks specified.
        old_checkpoint (`str`, *optional*):
            The name of the base checkpoint for the old model. Should be passed along when it can't be automatically
            recovered from the `model_type`.
    """
    # Retrieve all the old model info.
    model_info = retrieve_info_for_model(model_type, frameworks=frameworks)
    model_files = model_info["model_files"]
    old_model_patterns = model_info["model_patterns"]
    if old_checkpoint is not None:
        old_model_patterns.checkpoint = old_checkpoint
    if len(old_model_patterns.checkpoint) == 0:
        raise ValueError(
            "The old model checkpoint could not be recovered from the model type. Please pass it to the "
            "`old_checkpoint` argument."
        )

    keep_old_processing = True
    for processing_attr in ["image_processor_class", "feature_extractor_class", "processor_class", "tokenizer_class"]:
        if getattr(old_model_patterns, processing_attr) != getattr(new_model_patterns, processing_attr):
            keep_old_processing = False

    model_classes = model_info["model_classes"]

    # 1. We create the module for our new model.
    old_module_name = model_files["module_name"]
    module_folder = TRANSFORMERS_PATH / "models" / new_model_patterns.model_lower_cased
    os.makedirs(module_folder, exist_ok=True)

    files_to_adapt = model_files["model_files"]
    if keep_old_processing:
        files_to_adapt = [
            f
            for f in files_to_adapt
            if "tokenization" not in str(f)
            and "processing" not in str(f)
            and "feature_extraction" not in str(f)
            and "image_processing" not in str(f)
        ]

    os.makedirs(module_folder, exist_ok=True)
    for module_file in files_to_adapt:
        new_module_name = module_file.name.replace(
            old_model_patterns.model_lower_cased, new_model_patterns.model_lower_cased
        )
        dest_file = module_folder / new_module_name
        duplicate_module(
            module_file,
            old_model_patterns,
            new_model_patterns,
            dest_file=dest_file,
            add_copied_from=add_copied_from and "modeling" in new_module_name,
        )

    clean_frameworks_in_init(
        module_folder / "__init__.py", frameworks=frameworks, keep_processing=not keep_old_processing
    )

    # 2. We add our new model to the models init and the main init
    add_content_to_file(
        TRANSFORMERS_PATH / "models" / "__init__.py",
        f"    {new_model_patterns.model_lower_cased},",
        add_after=f"    {old_module_name},",
        exact_match=True,
    )
    add_model_to_main_init(
        old_model_patterns, new_model_patterns, frameworks=frameworks, with_processing=not keep_old_processing
    )

    # 3. Add test files
    files_to_adapt = model_files["test_files"]
    if keep_old_processing:
        files_to_adapt = [
            f
            for f in files_to_adapt
            if "tokenization" not in str(f)
            and "processor" not in str(f)
            and "feature_extraction" not in str(f)
            and "image_processing" not in str(f)
        ]

    def disable_fx_test(filename: Path) -> bool:
        with open(filename) as fp:
            content = fp.read()
        new_content = re.sub(r"fx_compatible\s*=\s*True", "fx_compatible = False", content)
        with open(filename, "w") as fp:
            fp.write(new_content)
        return content != new_content

    disabled_fx_test = False

    tests_folder = REPO_PATH / "tests" / "models" / new_model_patterns.model_lower_cased
    os.makedirs(tests_folder, exist_ok=True)
    with open(tests_folder / "__init__.py", "w"):
        pass

    for test_file in files_to_adapt:
        new_test_file_name = test_file.name.replace(
            old_model_patterns.model_lower_cased, new_model_patterns.model_lower_cased
        )
        dest_file = test_file.parent.parent / new_model_patterns.model_lower_cased / new_test_file_name
        duplicate_module(
            test_file,
            old_model_patterns,
            new_model_patterns,
            dest_file=dest_file,
            add_copied_from=False,
            attrs_to_remove=["pipeline_model_mapping", "is_pipeline_test_to_skip"],
        )
        disabled_fx_test = disabled_fx_test | disable_fx_test(dest_file)

    if disabled_fx_test:
        print(
            "The tests for symbolic tracing with torch.fx were disabled, you can add those once symbolic tracing works"
            " for your new model."
        )

    # 4. Add model to auto classes
    add_model_to_auto_classes(old_model_patterns, new_model_patterns, model_classes)

    # 5. Add doc file
    doc_file = REPO_PATH / "docs" / "source" / "en" / "model_doc" / f"{old_model_patterns.model_type}.md"
    duplicate_doc_file(doc_file, old_model_patterns, new_model_patterns, frameworks=frameworks)
    insert_model_in_doc_toc(old_model_patterns, new_model_patterns)

    # 6. Warn the user for duplicate patterns
    if old_model_patterns.model_type == old_model_patterns.checkpoint:
        print(
            "The model you picked has the same name for the model type and the checkpoint name "
            f"({old_model_patterns.model_type}). As a result, it's possible some places where the new checkpoint "
            f"should be, you have {new_model_patterns.model_type} instead. You should search for all instances of "
            f"{new_model_patterns.model_type} in the new files and check they're not badly used as checkpoints."
        )
    elif old_model_patterns.model_lower_cased == old_model_patterns.checkpoint:
        print(
            "The model you picked has the same name for the model type and the checkpoint name "
            f"({old_model_patterns.model_lower_cased}). As a result, it's possible some places where the new "
            f"checkpoint should be, you have {new_model_patterns.model_lower_cased} instead. You should search for "
            f"all instances of {new_model_patterns.model_lower_cased} in the new files and check they're not badly "
            "used as checkpoints."
        )
    if (
        old_model_patterns.model_type == old_model_patterns.model_lower_cased
        and new_model_patterns.model_type != new_model_patterns.model_lower_cased
    ):
        print(
            "The model you picked has the same name for the model type and the lowercased model name "
            f"({old_model_patterns.model_lower_cased}). As a result, it's possible some places where the new "
            f"model type should be, you have {new_model_patterns.model_lower_cased} instead. You should search for "
            f"all instances of {new_model_patterns.model_lower_cased} in the new files and check they're not badly "
            "used as the model type."
        )

    if not keep_old_processing and old_model_patterns.tokenizer_class is not None:
        print(
            "The constants at the start of the new tokenizer file created needs to be manually fixed. If your new "
            "model has a tokenizer fast, you will also need to manually add the converter in the "
            "`SLOW_TO_FAST_CONVERTERS` constant of `convert_slow_tokenizer.py`."
        )


def add_new_model_like_command_factory(args: Namespace):
    return AddNewModelLikeCommand(config_file=args.config_file, path_to_repo=args.path_to_repo)


class AddNewModelLikeCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        add_new_model_like_parser = parser.add_parser("add-new-model-like")
        add_new_model_like_parser.add_argument(
            "--config_file", type=str, help="A file with all the information for this model creation."
        )
        add_new_model_like_parser.add_argument(
            "--path_to_repo", type=str, help="When not using an editable install, the path to the Transformers repo."
        )
        add_new_model_like_parser.set_defaults(func=add_new_model_like_command_factory)

    def __init__(self, config_file=None, path_to_repo=None, *args):
        if config_file is not None:
            with open(config_file, "r", encoding="utf-8") as f:
                config = json.load(f)
            self.old_model_type = config["old_model_type"]
            self.model_patterns = ModelPatterns(**config["new_model_patterns"])
            self.add_copied_from = config.get("add_copied_from", True)
            self.frameworks = config.get("frameworks", get_default_frameworks())
            self.old_checkpoint = config.get("old_checkpoint", None)
        else:
            (
                self.old_model_type,
                self.model_patterns,
                self.add_copied_from,
                self.frameworks,
                self.old_checkpoint,
            ) = get_user_input()

        self.path_to_repo = path_to_repo

    def run(self):
        if self.path_to_repo is not None:
            # Adapt constants
            global TRANSFORMERS_PATH
            global REPO_PATH

            REPO_PATH = Path(self.path_to_repo)
            TRANSFORMERS_PATH = REPO_PATH / "src" / "transformers"

        create_new_model_like(
            model_type=self.old_model_type,
            new_model_patterns=self.model_patterns,
            add_copied_from=self.add_copied_from,
            frameworks=self.frameworks,
            old_checkpoint=self.old_checkpoint,
        )


def get_user_field(
    question: str,
    default_value: Optional[str] = None,
    is_valid_answer: Optional[Callable] = None,
    convert_to: Optional[Callable] = None,
    fallback_message: Optional[str] = None,
) -> Any:
    """
    A utility function that asks a question to the user to get an answer, potentially looping until it gets a valid
    answer.

    Args:
        question (`str`): The question to ask the user.
        default_value (`str`, *optional*): A potential default value that will be used when the answer is empty.
        is_valid_answer (`Callable`, *optional*):
            If set, the question will be asked until this function returns `True` on the provided answer.
        convert_to (`Callable`, *optional*):
            If set, the answer will be passed to this function. If this function raises an error on the procided
            answer, the question will be asked again.
        fallback_message (`str`, *optional*):
            A message that will be displayed each time the question is asked again to the user.

    Returns:
        `Any`: The answer provided by the user (or the default), passed through the potential conversion function.
    """
    if not question.endswith(" "):
        question = question + " "
    if default_value is not None:
        question = f"{question} [{default_value}] "

    valid_answer = False
    while not valid_answer:
        answer = input(question)
        if default_value is not None and len(answer) == 0:
            answer = default_value
        if is_valid_answer is not None:
            valid_answer = is_valid_answer(answer)
        elif convert_to is not None:
            try:
                answer = convert_to(answer)
                valid_answer = True
            except Exception:
                valid_answer = False
        else:
            valid_answer = True

        if not valid_answer:
            print(fallback_message)

    return answer


def convert_to_bool(x: str) -> bool:
    """
    Converts a string to a bool.
    """
    if x.lower() in ["1", "y", "yes", "true"]:
        return True
    if x.lower() in ["0", "n", "no", "false"]:
        return False
    raise ValueError(f"{x} is not a value that can be converted to a bool.")


def get_user_input():
    """
    Ask the user for the necessary inputs to add the new model.
    """
    model_types = list(auto_module.configuration_auto.MODEL_NAMES_MAPPING.keys())

    # Get old model type
    valid_model_type = False
    while not valid_model_type:
        old_model_type = input(
            "What is the model you would like to duplicate? Please provide the lowercase `model_type` (e.g. roberta): "
        )
        if old_model_type in model_types:
            valid_model_type = True
        else:
            print(f"{old_model_type} is not a valid model type.")
            near_choices = difflib.get_close_matches(old_model_type, model_types)
            if len(near_choices) >= 1:
                if len(near_choices) > 1:
                    near_choices = " or ".join(near_choices)
                print(f"Did you mean {near_choices}?")

    old_model_info = retrieve_info_for_model(old_model_type)
    old_tokenizer_class = old_model_info["model_patterns"].tokenizer_class
    old_image_processor_class = old_model_info["model_patterns"].image_processor_class
    old_feature_extractor_class = old_model_info["model_patterns"].feature_extractor_class
    old_processor_class = old_model_info["model_patterns"].processor_class
    old_frameworks = old_model_info["frameworks"]

    old_checkpoint = None
    if len(old_model_info["model_patterns"].checkpoint) == 0:
        old_checkpoint = get_user_field(
            "We couldn't find the name of the base checkpoint for that model, please enter it here."
        )

    model_name = get_user_field(
        "What is the name (with no special casing) for your new model in the paper (e.g. RoBERTa)? "
    )
    default_patterns = ModelPatterns(model_name, model_name)

    model_type = get_user_field(
        "What identifier would you like to use for the `model_type` of this model? ",
        default_value=default_patterns.model_type,
    )
    model_lower_cased = get_user_field(
        "What lowercase name would you like to use for the module (folder) of this model? ",
        default_value=default_patterns.model_lower_cased,
    )
    model_camel_cased = get_user_field(
        "What prefix (camel-cased) would you like to use for the model classes of this model (e.g. Roberta)? ",
        default_value=default_patterns.model_camel_cased,
    )
    model_upper_cased = get_user_field(
        "What prefix (upper-cased) would you like to use for the constants relative to this model? ",
        default_value=default_patterns.model_upper_cased,
    )
    config_class = get_user_field(
        "What will be the name of the config class for this model? ", default_value=f"{model_camel_cased}Config"
    )
    checkpoint = get_user_field(
        "Please give a checkpoint identifier (on the model Hub) for this new model (e.g. facebook/roberta-base): "
    )

    old_processing_classes = [
        c
        for c in [old_image_processor_class, old_feature_extractor_class, old_tokenizer_class, old_processor_class]
        if c is not None
    ]
    old_processing_classes = ", ".join(old_processing_classes)
    keep_processing = get_user_field(
        f"Will your new model use the same processing class as {old_model_type} ({old_processing_classes}) (yes/no)? ",
        convert_to=convert_to_bool,
        fallback_message="Please answer yes/no, y/n, true/false or 1/0. ",
    )
    if keep_processing:
        image_processor_class = old_image_processor_class
        feature_extractor_class = old_feature_extractor_class
        processor_class = old_processor_class
        tokenizer_class = old_tokenizer_class
    else:
        if old_tokenizer_class is not None:
            tokenizer_class = get_user_field(
                "What will be the name of the tokenizer class for this model? ",
                default_value=f"{model_camel_cased}Tokenizer",
            )
        else:
            tokenizer_class = None
        if old_image_processor_class is not None:
            image_processor_class = get_user_field(
                "What will be the name of the image processor class for this model? ",
                default_value=f"{model_camel_cased}ImageProcessor",
            )
        else:
            image_processor_class = None
        if old_feature_extractor_class is not None:
            feature_extractor_class = get_user_field(
                "What will be the name of the feature extractor class for this model? ",
                default_value=f"{model_camel_cased}FeatureExtractor",
            )
        else:
            feature_extractor_class = None
        if old_processor_class is not None:
            processor_class = get_user_field(
                "What will be the name of the processor class for this model? ",
                default_value=f"{model_camel_cased}Processor",
            )
        else:
            processor_class = None

    model_patterns = ModelPatterns(
        model_name,
        checkpoint,
        model_type=model_type,
        model_lower_cased=model_lower_cased,
        model_camel_cased=model_camel_cased,
        model_upper_cased=model_upper_cased,
        config_class=config_class,
        tokenizer_class=tokenizer_class,
        image_processor_class=image_processor_class,
        feature_extractor_class=feature_extractor_class,
        processor_class=processor_class,
    )

    add_copied_from = get_user_field(
        "Should we add # Copied from statements when creating the new modeling file (yes/no)? ",
        convert_to=convert_to_bool,
        default_value="yes",
        fallback_message="Please answer yes/no, y/n, true/false or 1/0.",
    )

    all_frameworks = get_user_field(
        "Should we add a version of your new model in all the frameworks implemented by"
        f" {old_model_type} ({old_frameworks}) (yes/no)? ",
        convert_to=convert_to_bool,
        default_value="yes",
        fallback_message="Please answer yes/no, y/n, true/false or 1/0.",
    )
    if all_frameworks:
        frameworks = None
    else:
        frameworks = get_user_field(
            "Please enter the list of framworks you want (pt, tf, flax) separated by spaces",
            is_valid_answer=lambda x: all(p in ["pt", "tf", "flax"] for p in x.split(" ")),
        )
        frameworks = list(set(frameworks.split(" ")))

    return (old_model_type, model_patterns, add_copied_from, frameworks, old_checkpoint)