File size: 99,500 Bytes
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
414907c
 
 
1b34eda
 
 
 
 
 
 
 
414907c
 
 
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61c0de1
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
693c995
1b34eda
 
693c995
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61c0de1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61c0de1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61c0de1
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
02840c5
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61c0de1
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61c0de1
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bc85147
1b34eda
 
bc85147
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ff16b23
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
02840c5
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
02840c5
1b34eda
 
ff16b23
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ff16b23
1b34eda
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
# coding=utf-8
# Copyright 2022 The OpenBMB Team The HuggingFace Inc. 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.
""" PyTorch CpmBee model."""
import copy
import math
from collections import UserDict
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn

from transformers.generation.beam_search import BeamHypotheses, BeamSearchScorer
from transformers.generation.streamers import BaseStreamer
from transformers.generation.utils import (
    GenerationConfig,
    LogitsProcessorList,
    StoppingCriteriaList,
    dist,
    inspect,
    is_deepspeed_zero3_enabled,
    warnings,
)
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, ModelOutput
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_cpmbee import CpmBeeConfig
from .tokenization_cpmbee import CpmBeeTokenizer


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "openbmb/cpm-bee-10b"
_CONFIG_FOR_DOC = "CpmBeeConfig"

CPMBEE_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "openbmb/cpm-bee-10b",
    "openbmb/cpm-bee-5b",
    "openbmb/cpm-bee-2b",
    "openbmb/cpm-bee-1b",
    # See all CPMBee models at https://huggingface.co/models?filter=cpmbee
]


class CpmBeeLinear(nn.Linear):
    def __init__(self, dim_in, dim_out, dtype):
        """
        Construct a linear for CPMBee. It contains a scale operation.
        """
        super().__init__(dim_in, dim_out, bias=False)
        self.dim_in = self.in_features = dim_in
        self.dim_out = self.out_features = dim_out

        self.weight = torch.nn.parameter.Parameter(torch.empty((dim_out, dim_in), dtype=dtype))

    def forward(self, x: torch.Tensor):
        """
        Args:
            x (`torch.Tensor` of shape `(batch, seq_len, dim_in)`): The input of linear layer
        Returns:
            `torch.Tensor` of shape `(batch, seq_len, dim_out)`: The output of the linear transform y.
        """
        x = nn.functional.linear(x, self.weight)
        x = x / math.sqrt(self.dim_in)
        return x


class CpmBeeLayerNorm(nn.Module):
    """
    We use Root Mean Square (RMS) Layer Normalization, please see https://arxiv.org/abs/1910.07467 for details."
    """

    def __init__(self, config: CpmBeeConfig):
        super().__init__()

        self.eps = config.eps
        self.dim_norm = config.hidden_size
        self.weight = nn.Parameter(torch.empty(config.hidden_size, dtype=config.torch_dtype))

    def forward(self, hidden_states: torch.Tensor):
        """
        Args:
            hidden_states (`torch.Tensor` of shape `(batch, seq_len, dim_in)`)
        """
        if hidden_states.size(-1) != self.dim_norm:
            raise AssertionError("hidden_states.size(-1) != self.dim_norm")
        old_dtype = hidden_states.dtype
        variance = hidden_states.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
        hidden_states = (hidden_states * torch.rsqrt(variance + self.eps)).to(old_dtype) * self.weight
        return hidden_states


class CpmBeeAttention(nn.Module):
    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        self.dim_model = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.dim_head = config.dim_head

        self.project_q = CpmBeeLinear(self.dim_model, self.num_heads * self.dim_head, dtype=config.torch_dtype)
        self.project_k = CpmBeeLinear(self.dim_model, self.num_heads * self.dim_head, dtype=config.torch_dtype)
        self.project_v = CpmBeeLinear(self.dim_model, self.num_heads * self.dim_head, dtype=config.torch_dtype)

        self.attention_out = CpmBeeLinear(self.num_heads * self.dim_head, self.dim_model, dtype=config.torch_dtype)

        self.softmax = torch.nn.Softmax(dim=-1)

        if config.dropout_p is not None:
            self.dropout = torch.nn.Dropout(p=config.dropout_p)
        else:
            self.dropout = None

    def forward(
        self,
        hidden_q: torch.Tensor,
        hidden_kv: torch.Tensor,
        attention_mask: torch.BoolTensor,
        position_bias: torch.Tensor,
        output_attentions: Optional[bool] = False,
        past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        use_cache: Optional[bool] = None,
    ):
        """
        Args:
            hidden_q (`torch.Tensor`):
                Input of transformer block(self-attention block). It can be the raw embedding of a batch of sequences.
            hidden_kv (`torch.Tensor` of shape `(batch, len_k, dim_model)`)):
                Tensor *key_value* and *query* of shape `(batch, len_k, dim_model)`
            attention_mask (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
                Avoid invalid areas to participate in the calculation of self-attention.
            position_bias (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
                Provide positional information to self-attention block.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            past_key_values (`Tuple[torch.Tensor, torch.Tensor]`, *optional*):
                Cached past key and value projection states.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        batch_size = hidden_q.size(0)
        len_q = hidden_q.size(1)
        len_k = hidden_kv.size(1)

        query = self.project_q(hidden_q)
        key = self.project_k(hidden_kv)
        value = self.project_v(hidden_kv)

        query = query.view(batch_size, len_q, self.num_heads, self.dim_head).permute(0, 2, 1, 3)
        key = key.view(batch_size, len_k, self.num_heads, self.dim_head).permute(0, 2, 1, 3)
        value = value.view(batch_size, len_k, self.num_heads, self.dim_head).permute(0, 2, 1, 3)

        if past_key_values is not None:
            key = torch.cat([past_key_values[0], key], dim=-2)
            value = torch.cat([past_key_values[1], value], dim=-2)
            len_k = key.size(-2)

        # (batch_size, num_heads, len_q, dim_head) @ (batch_size, num_heads, dim_head, len_k) -> (batch_size, num_heads, len_q, len_k)
        score = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.dim_head)
        score = score + position_bias

        score = torch.masked_fill(
            score,
            attention_mask.view(batch_size, 1, len_q, len_k) == torch.tensor(False),
            torch.scalar_tensor(float("-inf"), device=score.device, dtype=score.dtype),
        )
        score = self.softmax(score)

        score = torch.masked_fill(
            score,
            attention_mask.view(batch_size, 1, len_q, len_k) == torch.tensor(False),
            torch.scalar_tensor(0, device=score.device, dtype=score.dtype),
        )
        if output_attentions:
            attn_weights = score
        else:
            attn_weights = None

        if self.dropout is not None:
            score = self.dropout(score)

        # (batch_size, num_heads, len_q, len_k) @ (batch_size, num_heads, len_k, dim_head) -> (batch_size, num_heads, len_q, dim_head)
        score = torch.matmul(score, value)

        score = score.view(batch_size, self.num_heads, len_q, self.dim_head).permute(0, 2, 1, 3)
        score = score.contiguous().view(batch_size, len_q, self.num_heads * self.dim_head)

        score = self.attention_out(score)

        past_key_values = None
        if use_cache:
            past_key_values = (key, value)

        return score, attn_weights, past_key_values


class CpmBeeSelfAttentionBlock(nn.Module):
    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        self.layernorm_before_attention = CpmBeeLayerNorm(config)
        self.self_attention = CpmBeeAttention(config)
        if config.dropout_p:
            self.dropout = torch.nn.Dropout(config.dropout_p)
        else:
            self.dropout = None

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_bias: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
        past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        use_cache: Optional[bool] = None,
    ):
        """
        Args:
            hidden_states (`torch.Tensor` of shape `(batch, len_seq, dim_model)`):
                Input of transformer block(self-attention block). It can be the raw embedding of a batch of sequences.
            attention_mask (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
                Avoid invalid areas to participate in the calculation of self-attention.
            position_bias (`torch.Tensor` of shape `(batch, len_seq, len_seq)`):
                Provide positional information to self-attention block.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            past_key_values (`Tuple(torch.FloatTensor)`, *optional*):
                Cached past key and value projection states.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        outputs = self.layernorm_before_attention(hidden_states)
        outputs = self.self_attention(
            outputs, outputs, attention_mask, position_bias, output_attentions, past_key_values, use_cache
        )

        outputs, attn_weights, current_key_value = outputs

        if self.dropout is not None:
            outputs = self.dropout(outputs)
        hidden_states = (hidden_states + outputs) / 1.05

        return hidden_states, attn_weights, current_key_value


class CpmBeeDenseGatedACT(nn.Module):
    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        self.w_0 = CpmBeeLinear(config.hidden_size, config.dim_ff, dtype=config.torch_dtype)
        self.w_1 = CpmBeeLinear(config.hidden_size, config.dim_ff, dtype=config.torch_dtype)
        self.act = torch.nn.GELU()

    def forward(self, hidden_states: torch.Tensor):
        """Transform an input tensor from one feature space to another via a nonlinear operation

        Args:
            hidden_states (`torch.Tensor` of shape `(batch, seq_len, dim_in)`)
        """
        gate_score = self.act(self.w_0(hidden_states))
        hidden_states = self.w_1(hidden_states)

        hidden_states = gate_score * hidden_states
        return hidden_states


class CpmBeeFeedForward(nn.Module):
    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        self.w_in = CpmBeeDenseGatedACT(config)
        if config.dropout_p is not None:
            self.dropout = torch.nn.Dropout(config.dropout_p)
        else:
            self.dropout = None

        self.w_out = CpmBeeLinear(config.dim_ff, config.hidden_size, dtype=config.torch_dtype)

    def forward(self, hidden_states: torch.Tensor):
        """
        Args:
            hidden_states (`torch.Tensor` of shape `(batch, seq_len, dim_in)`)
        """
        hidden_states = self.w_in(hidden_states)

        if self.dropout is not None:
            hidden_states = self.dropout(hidden_states)

        hidden_states = self.w_out(hidden_states)

        return hidden_states


class CpmBeeFFNBlock(nn.Module):
    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        self.layernorm_before_ffn = CpmBeeLayerNorm(config)
        self.ffn = CpmBeeFeedForward(config)
        if config.dropout_p:
            self.dropout = torch.nn.Dropout(config.dropout_p)
        else:
            self.dropout = None

    def forward(
        self,
        hidden_states: torch.Tensor,
    ):
        """
        Args:
            hidden_states (`torch.Tensor` of shape `(batch, len_seq, dim_model)`):
                Hidden states before feed forward layer.
        """
        ln_outputs = self.layernorm_before_ffn(hidden_states)
        outputs = self.ffn(ln_outputs)
        if self.dropout is not None:
            outputs = self.dropout(outputs)
        hidden_states = (hidden_states + outputs) / 1.05
        return hidden_states


class CpmBeeTransformerBlock(nn.Module):
    def __init__(self, config: CpmBeeConfig, mask_att: bool = False, mask_ffn: bool = False):
        super().__init__()
        self.mask_att = mask_att
        self.mask_ffn = mask_ffn

        if not self.mask_att:
            self.self_att = CpmBeeSelfAttentionBlock(config)
        if not self.mask_ffn:
            self.ffn = CpmBeeFFNBlock(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_bias: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
        past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        use_cache: Optional[bool] = None,
    ):
        """
        Args:
            hidden_states (`torch.Tensor`):
                Input to the layer of shape `(batch, seq_len, dim_model)`
            attention_mask (`torch.Tensor`):
                Avoid invalid areas to participate in the calculation of shape `(batch, seq_len, seq_len)`
            position_bias (`torch.Tensor`):
                Provides position information to attention mechanism of shape `(num_heads, seq_len, seq_len)`
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            past_key_values (`Tuple[torch.Tensor, torch.Tensor])`, *optional*):
                Cached past key and value projection states
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        if not self.mask_att:
            hidden_states = self.self_att(
                hidden_states,
                attention_mask=attention_mask,
                position_bias=position_bias,
                output_attentions=output_attentions,
                past_key_values=past_key_values,
                use_cache=use_cache,
            )

            hidden_states, attn_weights, current_key_value = hidden_states
        else:
            attn_weights, current_key_value = None, (None, None)

        if not self.mask_ffn:
            hidden_states = self.ffn(hidden_states)

        return hidden_states, attn_weights, current_key_value


class CpmBeeEncoder(nn.Module):
    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        self.num_layers = config.num_hidden_layers
        if config.mask_modules is not None:
            assert len(config.mask_modules) == self.num_layers, "The total number of masks should equal to num_layers"
            for mask_module in config.mask_modules:
                assert len(mask_module) == 2, "For encoder, each mask should be (mask_att, mask_ffn)"
        else:
            config.mask_modules = [(False, False)] * self.num_layers

        self.layers = nn.ModuleList(
            [
                CpmBeeTransformerBlock(
                    config, mask_att=config.mask_modules[ith][0], mask_ffn=config.mask_modules[ith][1]
                )
                for ith in range(self.num_layers)
            ]
        )

        self.output_layernorm = CpmBeeLayerNorm(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_bias: torch.Tensor,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        past_key_values: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        use_cache: Optional[bool] = None,
    ):
        """
        Args:
            hidden_states (`torch.Tensor`):
                Input to the layer of shape `(batch, seq_len, dim_model)`
            attention_mask (`torch.Tensor`):
                Avoid invalid areas to participate in the calculation of shape `(batch, seq_len, seq_len)`
            position_bias (`torch.Tensor`):
                Provides position information to attention mechanism of shape `(num_heads, seq_len, seq_len)`
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers.
            past_key_values (`Tuple[torch.Tensor, torch.Tensor])`, *optional*):
                Cached past key and value projection states
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        current_key_values = () if use_cache else None

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            layer_outputs = layer(
                hidden_states,
                attention_mask,
                position_bias,
                output_attentions=output_attentions,
                past_key_values=past_key_values[i] if past_key_values else None,
                use_cache=use_cache,
            )
            hidden_states, attn_weights, current_key_value = layer_outputs
            if output_attentions:
                all_self_attns += (attn_weights,)
            if current_key_values is not None:
                current_key_values = current_key_values + (current_key_value,)

        hidden_states = self.output_layernorm(hidden_states)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        return hidden_states, current_key_values, all_hidden_states, all_self_attns


class CpmBeeBucketPositionBias(nn.Module):
    def __init__(self, config: CpmBeeConfig) -> None:
        super().__init__()

        self.num_heads = config.num_attention_heads
        self.num_buckets = config.position_bias_num_buckets
        self.num_segment_bucket = config.position_bias_num_segment_buckets
        self.max_distance = config.position_bias_max_distance

        self.relative_attention_bias = nn.Parameter(
            torch.empty(
                config.position_bias_num_buckets + config.position_bias_num_segment_buckets,
                config.num_attention_heads,
                dtype=config.torch_dtype,
            ),
        )

    def forward(self, query_pos: torch.Tensor, key_pos: torch.Tensor, rel_buckets: torch.Tensor):
        with torch.no_grad():
            batch = key_pos.size(0)
            keylen = key_pos.size(1)
            querylen = query_pos.size(1)

            if key_pos.size(0) != query_pos.size(0):
                raise AssertionError(
                    f"key_pos.size(0) should be equal to query_pos.size(0), but got {key_pos.size(0)} and {query_pos.size(0)}!"
                )
            if rel_buckets.size(0) != batch:
                raise AssertionError(
                    f"rel_buckets.size(0) should be equal to batch, but got {rel_buckets.size(0)} and {batch}!"
                )
            if rel_buckets.size(1) != querylen:
                raise AssertionError(
                    f"rel_buckets.size(1) should be equal to querylen, but got {rel_buckets.size(1)} and {querylen}!"
                )
            if rel_buckets.size(2) != keylen:
                raise AssertionError(
                    f"rel_buckets.size(2) should be equal to keylen, but got {rel_buckets.size(2)} and {keylen}!"
                )

            relative_position_bucket = rel_buckets - 1 + self.num_buckets

            inner_segment_bucket = self._position_bucket(
                key_pos[..., None, :] - query_pos[..., :, None],
                num_buckets=self.num_buckets,
                max_distance=self.max_distance,
            )
            relative_position_bucket = torch.where(
                rel_buckets == 0,
                inner_segment_bucket,
                relative_position_bucket,
            )

        embeds = nn.functional.embedding(relative_position_bucket, self.relative_attention_bias)
        embeds = embeds.permute(0, 3, 1, 2).contiguous()
        return embeds

    def _position_bucket(self, relative_position, num_buckets=32, max_distance=128):
        relative_buckets = 0
        num_buckets //= 2
        relative_buckets = (relative_position > 0).to(torch.int32) * num_buckets
        relative_position = torch.abs(relative_position)
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact
        relative_postion_if_large = max_exact + (
            torch.log(relative_position.float() / max_exact)
            / math.log(max_distance / max_exact)
            * (num_buckets - max_exact)
        ).to(torch.int32)
        relative_postion_if_large = torch.min(
            relative_postion_if_large,
            torch.full_like(relative_postion_if_large, num_buckets - 1),
        )
        relative_buckets += torch.where(is_small, relative_position.to(torch.int32), relative_postion_if_large)
        return relative_buckets


# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->CPMBee
class CpmBeeOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class CpmBeeRotaryEmbedding(nn.Module):
    """
    RotaryEmbedding embeds the unk token and special token. It will embeds the "...<mask>...<mask>...<unk>...<unk>..."
    to "...<mask_0>...<mask_1>...<unk_0>...<unk_1>..."" to help model to specify different special tokens and unk
    tokens.
    """

    def __init__(self, config: CpmBeeConfig):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, config.hidden_size, 2, dtype=torch.float32) / config.hidden_size))
        self.distance_scale = config.distance_scale
        self.dtype = config.torch_dtype
        self.inv_freq = inv_freq.to(config.torch_dtype)

    def forward(self, x: torch.Tensor, x_pos: torch.Tensor):
        inv_freq = self.inv_freq.to(device=x.device, dtype=x.dtype)

        x_pos = x_pos * self.distance_scale
        freqs = x_pos[..., None] * inv_freq[None, :]  # (..., dim/2)

        emb = torch.cat((freqs, freqs), dim=-1)  # (..., dim)
        emb_cos = emb.cos()  # (..., dim)
        emb_sin = emb.sin()  # (..., dim)

        rotate_x = torch.cat([-x[..., x.size(-1) // 2 :], x[..., : x.size(-1) // 2]], dim=-1)  # (..., dim)

        return x * emb_cos + rotate_x * emb_sin


class CpmBeeEmbeddingExt(nn.Embedding):
    """
    Contains a RotaryEmbedding.
    """

    def __init__(self, config: CpmBeeConfig):
        super().__init__(config.vocab_size, config.hidden_size, dtype=config.torch_dtype)
        self.dim_model = config.hidden_size
        self.rotary_emb = CpmBeeRotaryEmbedding(config)

    def forward(self, ids: torch.Tensor, ids_sub: torch.Tensor):
        embeds = super().forward(ids) / math.sqrt(self.dim_model)
        return self.rotary_emb(embeds, ids_sub)

    def projection(self, x: torch.Tensor, ext_table: Optional[torch.Tensor] = None):
        logits = nn.functional.linear(x / math.sqrt(self.dim_model), self.weight)
        if ext_table is not None:
            logits_ext = nn.functional.linear(x, ext_table)
            logits = torch.cat([logits, logits_ext], dim=-1)
        return logits


class CpmBeePreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = CpmBeeConfig
    base_model_prefix = "cpmbee"
    supports_gradient_checkpointing = True
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.init_std)
            if module.bias is not None:
                module.bias.data.zero_()
        # still needed
        elif isinstance(module, CpmBeeEmbeddingExt):
            module.weight.data.normal_(mean=0.0, std=self.config.init_std)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, CpmBeeLayerNorm):
            module.weight.data.fill_(1.0)
        elif isinstance(module, CpmBeeBucketPositionBias):
            module.relative_attention_bias.data.normal_(mean=0.0, std=self.config.init_std)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, CpmBeeEncoder):
            module.gradient_checkpointing = value


CPMBEE_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters
        config ([`~CpmBeeConfig`]): Model configuration class with all the parameters of the
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

CPMBEE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`CPMBeeTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        input_id_sub (`torch.Tensor` of shape `(batch_size, seq_len)`):
            Subscription of input sequence tokens in the vocabulary.

            Subscription of normal text will be zero while the special tokens of each group will be the 0, 1, 2, ...
            <ans_0>, <ans_1>, <ans_2> ... belongs to group <ans>. <mask_0>, <mask_1>, <mask_2> ... belongs to group
            <mask>.
        position (`torch.Tensor` of shape `(batch_size, seq_len)`):
            The position of input sequence tokens in the vocabulary for each segment. if segment1 is 0, 1, 2 and
            segment2 is 0, 1, 2, 3, the position will be 0, 1, 2, 0, 1, 2, 3
        context (`torch.Tensor` of shape `(batch_size, seq_len)`):
            Whether this token id is context or not. If is context, the value is 1. If not, the value is 0. If a token
            id is context, it does not need to be predicted.
        sample_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
            Give a sample id to every token id. The token ids with same sample ids belongs to the same sample.
        num_segments (`torch.Tensor` of shape `(batch_size, seq_len)`):
            Total number of segments in the current input.
        segment (`torch.Tensor` of shape `(batch_size, seq_len)`):
            Give a segment id to every token id. The token ids with same segment ids belongs to the same sample.

            Generally, a string key or value in input data will be a segment. For example, input {"input": "hello, ",
            "<ans>": ""}, the segments includes: "input", "hello, ", "<ans>" and "".
        segment_rel_offset (`torch.Tensor` of shape `(batch_size, seq_len)`):
            The offset of segment rel.
        segment_rel (`torch.Tensor` of shape `(batch_size, seq_len)`):
            The segment relevance. A relative implementation of measuring the importance of segments.
        past_states (`Dict[str, Union[torch.Tensor, List]]`):
            Store the history information including position, context, sample_ids, num_segments, segment and
            past_key_values.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            A dummy arguments for CPMBee. The `past_states` contains pre-computed hidden-states (key and values in the
            self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) and
            other history arguments to speed up sequential decoding.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare CPMBee Model outputting raw hidden-states without any specific head on top.",
    CPMBEE_START_DOCSTRING,
)
class CpmBeeModel(CpmBeePreTrainedModel):
    def __init__(self, config: CpmBeeConfig):
        super().__init__(config)
        if config.half:
            config.torch_dtype = torch.half
        else:
            config.torch_dtype = torch.float
        self.encoder = CpmBeeEncoder(config)
        self.input_embedding = CpmBeeEmbeddingExt(config)
        self.position_bias = CpmBeeBucketPositionBias(config)
        self.vocab_size = config.vocab_size
        self.post_init()

    def get_input_embeddings(self):
        return self.input_embedding

    def set_input_embeddings(self, embeddings, **kwargs):
        self.input_embedding = embeddings

    @add_start_docstrings_to_model_forward(CPMBEE_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: torch.Tensor,
        input_id_sub: Optional[torch.Tensor] = None,
        length: Optional[torch.Tensor] = None,
        context: Optional[torch.Tensor] = None,
        sample_ids: Optional[torch.Tensor] = None,
        num_segments: Optional[torch.Tensor] = None,
        segment: Optional[torch.Tensor] = None,
        segment_rel_offset: Optional[torch.Tensor] = None,
        segment_rel: Optional[torch.Tensor] = None,
        span: Optional[Dict] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        past_key_values: Optional[List] = None,
        use_cache: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        # dummy setting for common tests
        if input_id_sub is None:
            dtype, device = input_ids.dtype, input_ids.device
            batch, seq_length = input_ids.size()
            segment = torch.where(input_ids != 0, 2, 0).to(dtype=dtype, device=device)
            context = torch.full((batch, seq_length), 1, dtype=dtype, device=device)
            position = torch.arange(seq_length, dtype=dtype, device=device).repeat(batch, 1)
            input_id_sub = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            segment_rel_offset = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            segment_rel = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            num_segments = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            sample_ids = torch.zeros_like(input_ids)

        with torch.no_grad():
            batch = input_ids.size(0)
            seqlen = input_ids.size(1)
            device = input_ids.device

            # calc segment bucket
            segment_rel_2d = torch.masked_fill(
                segment[:, :, None] * num_segments[:, :, None]
                + segment[:, None, :]
                + segment_rel_offset[:, :, None],
                ~(
                    (sample_ids[:, :, None] == sample_ids[:, None, :])
                    & (span[:, None, :] == span[:, :, None])
                ),  # not in the same span or sample
                0,  # avoid torch.gather overflow
            ).view(batch, seqlen * seqlen)

            segment_bucket = torch.gather(
                input=segment_rel,
                dim=1,
                index=segment_rel_2d.long(),
            ).view(batch, seqlen, seqlen)

            segment_bucket.masked_fill_(
                ~(
                    (sample_ids[:, :, None] == sample_ids[:, None, :])
                    & (span[:, None, :] == span[:, :, None])
                ),  # not in the same span or sample
                1,  # bucket is used for in-context samples
            )

            # directional mask
            directional_mask_2d = torch.arange(seqlen, device=device) <= torch.arange(
                seqlen, device=device
            ).view(-1, 1)
            # sample mask
            sample_mask_2d = (sample_ids[:, :, None] == 0) | (
                sample_ids[:, :, None] == sample_ids[:, None, :]
            )
            # context mask
            attention_mask = context[:, None, :] | (
                context[:, :, None].logical_not() & directional_mask_2d.view(1, seqlen, seqlen)
            )
            # span mask
            attention_mask = (
                attention_mask & sample_mask_2d & (span[:, None, :] == span[:, :, None])
            )
            # length mask
            mask_1d = (
                torch.arange(seqlen, device=device)[None, :].repeat(batch, 1) < length[:, None]
            )
            attention_mask = (
                mask_1d.view(batch, seqlen, 1) & mask_1d.view(batch, 1, seqlen) & attention_mask
            )
            position = torch.arange(seqlen, device=device).expand(batch, seqlen)

        hidden_states = self.input_embedding(input_ids, input_id_sub)
        position_bias = self.position_bias(position, position, segment_bucket)
        hidden_states, present_key_values, all_hidden_states, all_attentions = self.encoder(
            hidden_states,
            attention_mask,
            position_bias,
            output_attentions,
            output_hidden_states,
            past_key_values=None,
            use_cache=False
        )

        if not return_dict:
            return tuple(
                v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None
            )

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=present_key_values,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
        )
    
    def inference(
        self,
        input_ids: torch.Tensor,
        input_id_sub: Optional[torch.Tensor] = None,
        position: Optional[torch.Tensor] = None,
        context: Optional[torch.Tensor] = None,
        sample_ids: Optional[torch.Tensor] = None,
        num_segments: Optional[torch.Tensor] = None,
        segment: Optional[torch.Tensor] = None,
        segment_rel_offset: Optional[torch.Tensor] = None,
        segment_rel: Optional[torch.Tensor] = None,
        past_states: Optional[Dict] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        past_key_values: Optional[List] = None,
        use_cache: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        # dummy setting for common tests
        if input_id_sub is None:
            dtype, device = input_ids.dtype, input_ids.device
            batch, seq_length = input_ids.size()
            segment = torch.where(input_ids != 0, 2, 0).to(dtype=dtype, device=device)
            context = torch.full((batch, seq_length), 1, dtype=dtype, device=device)
            position = torch.arange(seq_length, dtype=dtype, device=device).repeat(batch, 1)
            input_id_sub = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            segment_rel_offset = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            segment_rel = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            num_segments = torch.full((batch, seq_length), 0, dtype=dtype, device=device)
            sample_ids = torch.zeros_like(input_ids)

        with torch.no_grad():
            if past_states is None:
                present_position = position
                present_context = context
                present_sample_ids = sample_ids
                present_num_segments = num_segments
                present_segments = segment
                present_buffer = None
            else:
                present_position = torch.cat([past_states["buffer_position"], position], dim=-1)
                present_context = torch.cat([past_states["buffer_context"], context], dim=-1)
                present_sample_ids = torch.cat([past_states["buffer_sample_ids"], sample_ids], dim=-1)
                present_num_segments = torch.cat([past_states["buffer_num_segments"], num_segments], dim=-1)
                present_segments = torch.cat([past_states["buffer_segments"], segment], dim=-1)
                present_buffer = past_states["buffer"]

            batch = input_ids.size(0)
            len_q = input_ids.size(1)
            len_buffer = present_position.size(1)

            segment_rel_2d = torch.masked_fill(
                segment[:, :, None] * num_segments[:, :, None]
                + present_segments[:, None, :]
                + segment_rel_offset[:, :, None],
                ~((sample_ids[:, :, None] == present_sample_ids[:, None, :])),  # not in the same sample
                0,  # avoid torch.gather overflow
            ).view(batch, len_q * len_buffer)

            segment_bucket = torch.gather(
                input=segment_rel,
                dim=1,
                index=segment_rel_2d.long(),
            ).view(batch, len_q, len_buffer)

            segment_bucket.masked_fill_(
                ~((sample_ids[:, :, None] == present_sample_ids[:, None, :])),  # not in the same span or sample
                1,  # bucket is used for in-context samples
            )

            # directional mask
            directional_mask_2d = present_position[:, None, :] <= position[:, :, None]
            # sample mask
            sample_mask_2d = (sample_ids[:, :, None] == 0) | (sample_ids[:, :, None] == present_sample_ids[:, None, :])
            # context mask
            attention_mask = present_context[:, None, :] | (
                context[:, :, None].logical_not() & directional_mask_2d.view(batch, len_q, len_buffer)
            )
            # span mask
            attention_mask = attention_mask & sample_mask_2d
            # length mask
            mask_1d = present_num_segments != 0
            attention_mask = mask_1d.view(batch, 1, len_buffer) & attention_mask

        hidden_states = self.input_embedding(input_ids, input_id_sub)
        position_bias = self.position_bias(position, present_position, segment_bucket)
        hidden_states, present_key_values, all_hidden_states, all_attentions = self.encoder(
            hidden_states,
            attention_mask,
            position_bias,
            output_attentions,
            output_hidden_states,
            present_buffer,
            use_cache,
        )

        if not return_dict:
            return tuple(
                v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None
            )

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=present_key_values,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
        )


class CpmBeeBeamHypotheses(BeamHypotheses):
    def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
        """
        Override BeamHypotheses for CpmBee. The hyp to add is list but not tensor.
        """
        super().__init__(num_beams, length_penalty, early_stopping, max_length)

    def add(self, hyp: List, sum_logprobs: float, beam_indices: Optional[torch.LongTensor] = None):
        """
        Add a new hypothesis to the list.
        """
        score = sum_logprobs / (len(hyp) ** self.length_penalty)
        if len(self) < self.num_beams or score > self.worst_score:
            self.beams.append((score, hyp, beam_indices))
            if len(self) > self.num_beams:
                sorted_next_scores = sorted([(s, idx) for idx, (s, _, _) in enumerate(self.beams)])
                del self.beams[sorted_next_scores[0][1]]
                self.worst_score = sorted_next_scores[1][0]
            else:
                self.worst_score = min(score, self.worst_score)


class CpmBeeBeamSearchScorer(BeamSearchScorer):
    """
    Override BeamSearchScorer for CPMBee to support:
    1. Replace beam_tokens by beam_states, containing `idx`, `ans`, `nx_token_id`...
    2. The `process` will update the beam_states
    3. The `finalize` will just return the best hypotheses as a list.
    """

    def __init__(
        self,
        batch_size: int,
        num_beams: int,
        device: torch.device,
        length_penalty: Optional[float] = 1.0,
        do_early_stopping: Optional[Union[bool, str]] = False,
        num_beam_hyps_to_keep: Optional[int] = 1,
        num_beam_groups: Optional[int] = 1,
        max_length: Optional[int] = None,
        **model_kwargs,
    ):
        self.num_beams = num_beams
        self.device = device
        self.length_penalty = length_penalty
        self.do_early_stopping = do_early_stopping
        self.num_beam_hyps_to_keep = num_beam_hyps_to_keep
        self.num_beam_groups = num_beam_groups
        self.group_size = self.num_beams // self.num_beam_groups

        self._is_init = False
        self._beam_hyps = [
            CpmBeeBeamHypotheses(
                num_beams=self.num_beams,
                length_penalty=self.length_penalty,
                early_stopping=self.do_early_stopping,
                max_length=max_length,
            )
            for _ in range(batch_size)
        ]
        self._done = torch.tensor([False for _ in range(batch_size)], dtype=torch.bool, device=self.device)

        self.beam_states = []
        for sent_id in range(batch_size):
            instance_beam_states = []

            for _ in range(self.num_beams):
                instance_beam_states.append(
                    {
                        "idx": 0,
                        "ans": [],
                        "nx_token_id": 6,
                        "nx_token_sub": 0,
                        "nx_segment_id": model_kwargs["other_info"][sent_id]["predict_segments"][0][0],
                        "nx_position": 0,
                    }
                )
            self.beam_states.append(instance_beam_states)

    def process(
        self,
        batch_size: int,
        cur_len: int,
        _next_scores: torch.FloatTensor,
        next_scores: torch.FloatTensor,
        next_tokens: torch.LongTensor,
        vocab_size: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        bos_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[int]]] = None,
        max_length: Optional[int] = None,
        ext_table_sub_cpu: Optional[torch.Tensor] = None,
        ext_table_ids_cpu: Optional[torch.Tensor] = None,
        **model_kwargs,
    ) -> Tuple[torch.Tensor]:
        next_beam_state = []
        for sent_id in range(batch_size):
            self._done[sent_id] = self._done[sent_id] or self._beam_hyps[sent_id].is_done(
                next_scores[sent_id].max().item(), cur_len
            )
            if self._done[sent_id]:
                next_beam_state.append(
                    [
                        (
                            {
                                "idx": 0,
                                "ans": [],
                                "nx_token_id": pad_token_id,
                                "nx_token_sub": 0,
                                "nx_segment_id": 0,
                                "nx_position": 0,
                            },
                            0,
                            0,
                        )
                    ]
                    * self.num_beams
                )
                continue

            next_instance_beam_states = []

            for idx, value in zip(next_tokens[sent_id], next_scores[sent_id]):
                beam_id = torch.div(idx, _next_scores.size(-1), rounding_mode="floor").item()
                word_id = (idx % _next_scores.size(-1)).item()

                curr_info = self.beam_states[sent_id][beam_id]
                if (
                    word_id == eos_token_id
                    and (curr_info["idx"] + 1 == len(model_kwargs["other_info"][sent_id]["predict_segments"]))
                ) or cur_len == max_length:
                    self._beam_hyps[sent_id].add(
                        self.beam_states[sent_id][beam_id]["ans"]
                        + [
                            (
                                word_id,
                                model_kwargs["other_info"][sent_id]["predict_segments"][curr_info["idx"]][1],
                            )
                        ],
                        value.item(),
                    )
                elif word_id == eos_token_id:
                    next_instance_beam_states.append(
                        (
                            {
                                "idx": curr_info["idx"] + 1,
                                "ans": curr_info["ans"]
                                + [
                                    (
                                        word_id,
                                        model_kwargs["other_info"][sent_id]["predict_segments"][curr_info["idx"]][1],
                                    )
                                ],
                                "nx_token_id": bos_token_id,
                                "nx_token_sub": 0,
                                "nx_segment_id": model_kwargs["other_info"][sent_id]["predict_segments"][
                                    curr_info["idx"] + 1
                                ][0],
                                "nx_position": 0,
                            },
                            value.item(),
                            sent_id * self.num_beams + beam_id,
                        )
                    )

                else:
                    raw_word_id = word_id
                    word_id_sub = 0
                    if word_id >= vocab_size:
                        word_id -= vocab_size
                        word_id_sub = int(ext_table_sub_cpu[word_id].item())
                        word_id = int(ext_table_ids_cpu[word_id].item())

                    next_instance_beam_states.append(
                        (
                            {
                                "idx": curr_info["idx"],
                                "ans": curr_info["ans"]
                                + [
                                    (
                                        raw_word_id,
                                        model_kwargs["other_info"][sent_id]["predict_segments"][curr_info["idx"]][1],
                                    )
                                ],
                                "nx_token_id": word_id,
                                "nx_token_sub": word_id_sub,
                                "nx_segment_id": curr_info["nx_segment_id"],
                                "nx_position": curr_info["nx_position"] + 1,
                            },
                            value.item(),
                            sent_id * self.num_beams + beam_id,
                        )
                    )

                if len(next_instance_beam_states) == self.num_beams:
                    break
            assert len(next_instance_beam_states) == 0 if cur_len == max_length else self.num_beams
            next_beam_state.append(next_instance_beam_states)

        if cur_len == max_length:
            return None

        beam_reorder_idx = []
        beam_new_scores = []
        beam_states = []
        for sent_id in range(batch_size):
            instance_beam_states = []
            for beam_id in range(self.num_beams):
                state, value, beam_idx = next_beam_state[sent_id][beam_id]
                beam_reorder_idx.append(beam_idx)
                beam_new_scores.append(value)
                instance_beam_states.append(state)
            beam_states.append(instance_beam_states)
        self.beam_states = beam_states

        return UserDict(
            {
                "next_beam_scores": torch.tensor(beam_new_scores, device=self.device).view(-1),
                "next_beam_states": beam_states,
                "next_beam_indices": torch.tensor(beam_reorder_idx, dtype=torch.int32, device=self.device).view(-1),
            }
        )

    def finalize(self) -> Tuple[torch.LongTensor]:
        results = []
        for _, hypotheses in enumerate(self._beam_hyps):
            best_hyp = max(hypotheses.beams, key=lambda x: x[0])[1]
            results.append(best_hyp)
        return results

    @staticmethod
    def apply_repetition_penalty(
        logits,
        batch_size,
        num_beams,
        prev_output_tokens,
        repetition_penalty,
        start_idx=None,
        end_idx=None,
        window_size=None,
    ):
        # only conduct repetition penalty for the output
        assert repetition_penalty >= 1, "repetition penalty coefficient should >= 1"
        # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
        for i in range(batch_size * num_beams):
            if start_idx is None or end_idx is None:
                output_tokens = prev_output_tokens[i].tolist()
            else:
                if end_idx >= start_idx:
                    if window_size:
                        output_tokens = prev_output_tokens[i][
                            max(start_idx, end_idx + 1 - window_size) : end_idx + 1
                        ].tolist()
                    else:
                        output_tokens = prev_output_tokens[i][start_idx : end_idx + 1].tolist()
                else:
                    output_tokens = []
            for previous_token in set(output_tokens):
                # if score < 0 then repetition penalty has to
                # multiplied to reduce the previous token probability
                if logits[i, previous_token] < 0:
                    logits[i, previous_token] *= repetition_penalty
                else:
                    logits[i, previous_token] /= repetition_penalty


@add_start_docstrings(
    """
    The CPMBee Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
    """,
    CPMBEE_START_DOCSTRING,
)
class CpmBeeForCausalLM(CpmBeePreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]

    def __init__(self, config: CpmBeeConfig):
        super().__init__(config)
        self.cpmbee = CpmBeeModel(config)

        # lm_head.weight is tied to cpmbee.input_embedding.weight
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()

    @add_start_docstrings_to_model_forward(CPMBEE_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        input_id_sub: Optional[torch.Tensor] = None,
        length: Optional[torch.Tensor] = None,
        context: Optional[torch.Tensor] = None,
        sample_ids: Optional[torch.Tensor] = None,
        num_segments: Optional[torch.Tensor] = None,
        segment: Optional[torch.Tensor] = None,
        segment_rel_offset: Optional[torch.Tensor] = None,
        segment_rel: Optional[torch.Tensor] = None,
        span: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        past_key_values: Optional[List] = None,
        use_cache: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
        ext_table_ids: Optional[torch.Tensor] = None,  # (ext_table_size) int32
        ext_table_sub: Optional[torch.Tensor] = None,  # (ext_table_size) int32
        **kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Indices of input sequence tokens in the vocabulary.

                Indices can be obtained using [`CPMBeeTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            input_id_sub (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Subscription of input sequence tokens in the vocabulary.

                Subscription of normal text will be zero while the special tokens of each group will be the 0, 1, 2,
                ... <ans_0>, <ans_1>, <ans_2> ... belongs to group <ans>. <mask_0>, <mask_1>, <mask_2> ... belongs to
                group <mask>.
            length (`torch.Tensor` of shape `(batch_size)`):
                The length of sequences in batch.
            context (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Whether this token id is context or not. If is context, the value is 1. If not, the value is 0. If a
                token id is context, it does not need to be predicted.
            sample_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Give a sample id to every token id. The token ids with same sample ids belongs to the same sample.
            num_segments (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Total number of segments in the current input.
            segment (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Give a segment id to every token id. The token ids with same segment ids belongs to the same sample.

                Generally, a string key or value in input data will be a segment. For example, input {"input": "hello,
                ", "<ans>": ""}, the segments includes: "input", "hello, ", "<ans>" and "".
            segment_rel_offset (`torch.Tensor` of shape `(batch_size, seq_len)`):
                The offset of segment rel.
            segment_rel (`torch.Tensor` of shape `(batch_size, seq_len)`):
                The segment relevance. A relative implementation of measuring the importance of segments.
            span (`Dict[str, Union[torch.Tensor, List]]`):
                Span will record every input_ids shape.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers.
            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                A dummy arguments for CPMBee. The `past_states` contains pre-computed hidden-states (key and values in
                the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values`
                input) and other history arguments to speed up sequential decoding.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            ext_table_ids (`torch.Tensor`, *optional*):
                ext_table ids for embedding projection.
            ext_table_sub (`torch.Tensor`, *optional*):
                ext_table subscriptions for embedding projection.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        model_output = self.cpmbee(
            input_ids,
            input_id_sub,
            length,
            context,
            sample_ids,
            num_segments,
            segment,
            segment_rel_offset,
            segment_rel,
            span,
            output_attentions,
            output_hidden_states,
            past_key_values,
            use_cache,
            return_dict,
        )
        hidden_states = model_output.last_hidden_state if return_dict else model_output[0]

        if ext_table_ids is not None:
            ext_table = self.cpmbee.input_embedding(ext_table_ids, ext_table_sub)
        else:
            ext_table = None
        logits = self.cpmbee.input_embedding.projection(hidden_states, ext_table)

        loss = None
        if labels is not None:
            loss_func = nn.CrossEntropyLoss()
            loss = loss_func(logits.view(-1, logits.size(-1)), labels.long().view(-1))

        if not return_dict:
            output = (logits,) + model_output[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=model_output.past_key_values,
            hidden_states=model_output.hidden_states,
            attentions=model_output.attentions,
        )
    
    def inference(
        self,
        input_ids: Optional[torch.Tensor] = None,
        input_id_sub: Optional[torch.Tensor] = None,
        position: Optional[torch.Tensor] = None,
        context: Optional[torch.Tensor] = None,
        sample_ids: Optional[torch.Tensor] = None,
        num_segments: Optional[torch.Tensor] = None,
        segment: Optional[torch.Tensor] = None,
        segment_rel_offset: Optional[torch.Tensor] = None,
        segment_rel: Optional[torch.Tensor] = None,
        past_states: Optional[Dict] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        past_key_values: Optional[List] = None,
        use_cache: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
        ext_table_ids: Optional[torch.Tensor] = None,  # (ext_table_size) int32
        ext_table_sub: Optional[torch.Tensor] = None,  # (ext_table_size) int32
        **kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Indices of input sequence tokens in the vocabulary.

                Indices can be obtained using [`CPMBeeTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            input_id_sub (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Subscription of input sequence tokens in the vocabulary.

                Subscription of normal text will be zero while the special tokens of each group will be the 0, 1, 2,
                ... <ans_0>, <ans_1>, <ans_2> ... belongs to group <ans>. <mask_0>, <mask_1>, <mask_2> ... belongs to
                group <mask>.
            position (`torch.Tensor` of shape `(batch_size, seq_len)`):
                The position of input sequence tokens in the vocabulary for each segment. if segment1 is 0, 1, 2 and
                segment2 is 0, 1, 2, 3, the position will be 0, 1, 2, 0, 1, 2, 3
            context (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Whether this token id is context or not. If is context, the value is 1. If not, the value is 0. If a
                token id is context, it does not need to be predicted.
            sample_ids (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Give a sample id to every token id. The token ids with same sample ids belongs to the same sample.
            num_segments (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Total number of segments in the current input.
            segment (`torch.Tensor` of shape `(batch_size, seq_len)`):
                Give a segment id to every token id. The token ids with same segment ids belongs to the same sample.

                Generally, a string key or value in input data will be a segment. For example, input {"input": "hello,
                ", "<ans>": ""}, the segments includes: "input", "hello, ", "<ans>" and "".
            segment_rel_offset (`torch.Tensor` of shape `(batch_size, seq_len)`):
                The offset of segment rel.
            segment_rel (`torch.Tensor` of shape `(batch_size, seq_len)`):
                The segment relevance. A relative implementation of measuring the importance of segments.
            past_states (`Dict[str, Union[torch.Tensor, List]]`):
                Store the history information including position, context, sample_ids, num_segments, segment and
                past_key_values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers.
            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                A dummy arguments for CPMBee. The `past_states` contains pre-computed hidden-states (key and values in
                the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values`
                input) and other history arguments to speed up sequential decoding.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            ext_table_ids (`torch.Tensor`, *optional*):
                ext_table ids for embedding projection.
            ext_table_sub (`torch.Tensor`, *optional*):
                ext_table subscriptions for embedding projection.

        Example:

        Text Generation with CpmBeeForCausalLM.
        ```python
        >>> from transformers import CpmBeeTokenizer, CpmBeeForCausalLM

        >>> texts = {"input": "今天天气不错,", "<ans>": ""}
        >>> model = CpmBeeForCausalLM.from_pretrained("openbmb/cpm-bee-10b")
        >>> tokenizer = CPMBeeTokenizer.from_pretrained("openbmb/cpm-bee-10b")
        >>> output_texts = model.generate({"input": "今天天气不错,", "<ans>": ""}, tokenizer)
        >>> print(output_texts)
        {'input': '今天天气不错,', '<ans>': '适合睡觉。'}
        ```
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        model_output = self.cpmbee.inference(
            input_ids,
            input_id_sub,
            position,
            context,
            sample_ids,
            num_segments,
            segment,
            segment_rel_offset,
            segment_rel,
            past_states,
            output_attentions,
            output_hidden_states,
            past_key_values,
            use_cache,
            return_dict,
        )
        hidden_states = model_output.last_hidden_state if return_dict else model_output[0]

        if ext_table_ids is not None:
            ext_table = self.cpmbee.input_embedding(ext_table_ids, ext_table_sub)
        else:
            ext_table = None
        logits = self.cpmbee.input_embedding.projection(hidden_states, ext_table)

        loss = None
        if labels is not None:
            loss_func = nn.CrossEntropyLoss()
            loss = loss_func(logits.view(-1, logits.size(-1)), labels.view(-1))

        if not return_dict:
            output = (logits,) + model_output[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=model_output.past_key_values,
            hidden_states=model_output.hidden_states,
            attentions=model_output.attentions,
        )

    def get_input_embeddings(self):
        return self.cpmbee.input_embedding

    def set_input_embeddings(self, embeddings):
        self.cpmbee.input_embedding = embeddings

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.Tensor,
        batch_size: int,
        beam_scorer: CpmBeeBeamSearchScorer = None,
        input_id_subs: Optional[torch.Tensor] = None,
        input_pos: Optional[torch.Tensor] = None,
        segment_ids: Optional[torch.Tensor] = None,
        batch_ext_table_ids: Optional[torch.Tensor] = None,
        batch_ext_table_sub: Optional[torch.Tensor] = None,
        other_info: Optional[Dict] = None,
        **model_kwargs,
    ):
        """
        Choose the current input according to beam states.
        """
        # init preparation
        context = model_kwargs.get("context")
        sample_ids = model_kwargs.get("sample_ids")
        segment_rel_offset = model_kwargs.get("segment_rel_offset")
        num_segments = model_kwargs.get("num_segments")
        segment_rel = model_kwargs.get("segment_rel")
        past_states = model_kwargs.get("past_states", None)
        past_key_values = model_kwargs.get("past_key_values", None)
        _input_ids = input_ids

        # update input in generation
        if beam_scorer is not None:
            tmp_input = []
            tmp_input_sub = []
            tmp_position = []
            tmp_segment = []
            for sent_id in range(batch_size):
                for beam_id in range(beam_scorer.num_beams):
                    tmp_input.append(beam_scorer.beam_states[sent_id][beam_id]["nx_token_id"])
                    tmp_input_sub.append(beam_scorer.beam_states[sent_id][beam_id]["nx_token_sub"])
                    tmp_position.append(beam_scorer.beam_states[sent_id][beam_id]["nx_position"])
                    tmp_segment.append(beam_scorer.beam_states[sent_id][beam_id]["nx_segment_id"])

            model_kwargs["input_id_subs"] = input_id_subs = torch.tensor(
                tmp_input_sub, dtype=torch.int32, device=self.device
            ).view(batch_size * beam_scorer.num_beams, 1)
            model_kwargs["input_pos"] = input_pos = torch.tensor(
                tmp_position, dtype=torch.int32, device=self.device
            ).view(batch_size * beam_scorer.num_beams, 1)
            model_kwargs["segment_ids"] = segment_ids = torch.tensor(
                tmp_segment, dtype=torch.int32, device=self.device
            ).view(batch_size * beam_scorer.num_beams, 1)
            input_ids = torch.cat(
                [
                    input_ids,
                    torch.tensor(tmp_input, dtype=torch.int32, device=self.device).view(
                        batch_size * beam_scorer.num_beams, 1
                    ),
                ],
                dim=-1,
            )
            _input_ids = input_ids[:, -1:]

        return {
            "input_ids": _input_ids,
            "input_id_sub": input_id_subs,
            "position": input_pos,
            "context": context,
            "sample_ids": sample_ids,
            "segment_rel_offset": segment_rel_offset,
            "segment": segment_ids,
            "num_segments": num_segments,
            "segment_rel": segment_rel,
            "use_cache": True,
            "past_key_values": past_key_values,
            "ext_table_ids": batch_ext_table_ids,
            "ext_table_sub": batch_ext_table_sub,
            "past_states": past_states,
        }, input_ids

    def _update_model_kwargs_for_generation(
        self,
        outputs: ModelOutput,
        model_inputs=None,
        **model_kwargs,
    ) -> Dict[str, Any]:
        """
        Concatenate the history input and current input.
        """

        old_past_states = model_kwargs["past_states"]
        model_kwargs["past_states"] = {
            "buffer_position": torch.cat([old_past_states["buffer_position"], model_inputs["position"]], dim=-1),
            "buffer_context": torch.cat([old_past_states["buffer_context"], model_inputs["context"]], dim=-1),
            "buffer_sample_ids": torch.cat([old_past_states["buffer_sample_ids"], model_inputs["sample_ids"]], dim=-1),
            "buffer_num_segments": torch.cat(
                [old_past_states["buffer_num_segments"], model_inputs["num_segments"]], dim=-1
            ),
            "buffer_segments": torch.cat([old_past_states["buffer_segments"], model_inputs["segment"]], dim=-1),
            "buffer": outputs.past_key_values,
        }

        return model_kwargs

    def _reorder_cache(self, past_key_values: Dict, beam_idx: torch.Tensor):
        beam_idx = beam_idx.tolist()
        for kw in past_key_values.keys():
            if kw == "buffer":
                buf_list = past_key_values[kw]
                nw_buf_list = []
                for buf in buf_list:
                    if buf == (None, None):
                        nw_buf_list.append((None, None))
                    else:
                        k_buf, v_buf = buf
                        nw_buf_list.append((k_buf[beam_idx, :], v_buf[beam_idx, :]))
                past_key_values[kw] = nw_buf_list
            else:
                past_key_values[kw] = past_key_values[kw][beam_idx, :]

        return past_key_values

    @staticmethod
    def _expand_inputs_for_generation(
        expand_size: int = 1,
        is_encoder_decoder: bool = False,
        input_ids: Optional[torch.LongTensor] = None,
        **model_kwargs,
    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
        """Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""

        # do not expand ext_table_ids and ext_table_sub
        def _expand_dict_for_generation(dict_to_expand):
            for key in dict_to_expand:
                if (
                    dict_to_expand[key] is not None
                    and isinstance(dict_to_expand[key], torch.Tensor)
                    and "ext_table" not in key
                ):
                    dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
            return dict_to_expand

        if input_ids is not None:
            input_ids = input_ids.repeat_interleave(expand_size, dim=0)

        model_kwargs = _expand_dict_for_generation(model_kwargs)

        if is_encoder_decoder:
            if model_kwargs.get("encoder_outputs") is None:
                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
            model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])

        return input_ids, model_kwargs

    def adjust_logits_during_generation(
        self,
        logits: torch.FloatTensor,
        batch_size: int,
        beam_size: int,
        vocab_size: int,
        ext_table_ids: torch.Tensor,
        **model_kwargs,
    ) -> torch.FloatTensor:
        """
        Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
        """
        for sent_id in range(batch_size):
            if 1 not in model_kwargs["other_info"][sent_id]["ext_table"]:
                # unk is not allowed, mask unk
                logits[sent_id * beam_size : (sent_id + 1) * beam_size, 1] = -10000
            ext_ids = set()
            for v in model_kwargs["other_info"][sent_id]["ext_table"].keys():
                ext_ids.add(v)
            for ext_id in range(vocab_size, vocab_size + ext_table_ids.size(0)):
                if ext_id not in ext_ids:
                    logits[sent_id * beam_size : (sent_id + 1) * beam_size, ext_id] = -10000
        return logits

    def beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: CpmBeeBeamSearchScorer,
        repetition_penalty: Optional[float] = 1.0,
        logits_processor: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[int]]] = None,
        bos_token_id: Optional[Union[int, List[int]]] = None,
        vocab_size: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        synced_gpus: bool = False,
        **model_kwargs,
    ) -> List:
        """
        Override the beam_search for CPMBee.
        """
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
        bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
        vocab_size = vocab_size if vocab_size is not None else self.generation_config.vocab_size
        max_length = max_length if max_length is not None else self.generation_config.max_new_tokens
        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
        output_attentions = (
            output_attentions if output_attentions is not None else self.generation_config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate
            if return_dict_in_generate is not None
            else self.generation_config.return_dict_in_generate
        )

        batch_size = len(beam_scorer._beam_hyps)
        num_beams = beam_scorer.num_beams

        batch_beam_size, cur_len = input_ids.shape

        if num_beams * batch_size != batch_beam_size:
            raise ValueError(
                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
            )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        beam_indices = (
            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
        )
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
        # of the first beam are considered to avoid sampling the exact same tokens across all beams.
        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=self.device)
        beam_scores[:, 1:] = -1e9
        beam_scores = beam_scores.view((batch_size * num_beams,))

        this_peer_finished = False  # used by synced_gpus only

        # init inference
        model_inputs, input_ids = self.prepare_inputs_for_generation(input_ids, batch_size, **model_kwargs)
        pred_start_index = input_ids.size(-1)
        outputs = self.inference(
            **model_inputs,
            return_dict=True,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        # update model_kwargs
        model_kwargs["past_states"] = {
            "buffer_position": model_inputs["position"],
            "buffer_context": model_inputs["context"],
            "buffer_sample_ids": model_inputs["sample_ids"],
            "buffer_num_segments": model_inputs["num_segments"],
            "buffer_segments": model_inputs["segment"],
            "buffer": outputs.past_key_values,
        }
        model_kwargs["context"] = torch.ones(batch_beam_size, dtype=torch.bool, device=self.device).view(
            batch_beam_size, 1
        )
        model_kwargs["sample_ids"] = torch.zeros(batch_beam_size, dtype=torch.int32, device=self.device).view(
            batch_beam_size, 1
        )
        model_kwargs["num_segments"] = model_kwargs["num_segments"][:, -1:]
        model_kwargs["segment_rel_offset"] = model_kwargs["segment_rel_offset"][:, -1:]
        model_kwargs["past_key_values"] = outputs.past_key_values

        ext_table_ids_cpu = model_inputs["ext_table_ids"].cpu()
        ext_table_sub_cpu = model_inputs["ext_table_sub"].cpu()

        cur_len = 0
        while True:
            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            model_inputs, input_ids = self.prepare_inputs_for_generation(
                input_ids, batch_size, beam_scorer, **model_kwargs
            )

            outputs = self.inference(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            next_token_logits = outputs.logits[:, -1, :]

            if all(beam_scorer._done):
                break
            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
            next_token_logits = self.adjust_logits_during_generation(
                next_token_logits, batch_size, num_beams, vocab_size, ext_table_ids_cpu, **model_kwargs
            )

            # repetition_penalty
            beam_scorer.apply_repetition_penalty(
                next_token_logits,
                batch_size,
                num_beams,
                input_ids,
                repetition_penalty,
                pred_start_index,
                input_ids.size(-1) - 1,
                None,
            )

            _next_token_scores = nn.functional.log_softmax(
                next_token_logits, dim=-1
            )  # (batch_size * num_beams, vocab_size)

            next_token_scores_processed = logits_processor(input_ids, _next_token_scores)
            # next_token_scores_processed = _next_token_scores
            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(_next_token_scores)

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores_processed,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # reshape for beam search
            next_token_scores = next_token_scores.view(batch_size, -1)

            # Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
            next_token_scores, next_tokens = torch.topk(
                next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
            )

            beam_outputs = beam_scorer.process(
                batch_size,
                cur_len,
                _next_token_scores,
                next_token_scores,
                next_tokens,
                vocab_size=vocab_size,
                pad_token_id=pad_token_id,
                bos_token_id=bos_token_id,
                eos_token_id=eos_token_id,
                max_length=max_length,
                ext_table_ids_cpu=ext_table_ids_cpu,
                ext_table_sub_cpu=ext_table_sub_cpu,
                **model_kwargs,
            )
            if beam_outputs is None:
                break
            beam_idx = beam_outputs["next_beam_indices"]
            beam_scores = beam_outputs["next_beam_scores"]

            input_ids = input_ids[beam_idx.tolist(), :]
            model_kwargs = self._update_model_kwargs_for_generation(outputs, model_inputs, **model_kwargs)
            if model_kwargs["past_states"] is not None:
                model_kwargs["past_states"] = self._reorder_cache(model_kwargs["past_states"], beam_idx)

            if return_dict_in_generate and output_scores:
                beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))

            cur_len += 1

            if beam_scorer.is_done or cur_len == max_length + 1:
                if not synced_gpus:
                    break
                else:
                    this_peer_finished = True

        sequence_outputs = beam_scorer.finalize()

        return sequence_outputs

    def _generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        repetition_penalty: Optional[float] = 1.0,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
        synced_gpus: Optional[bool] = None,
        streamer: Optional["BaseStreamer"] = None,
        **kwargs,
    ) -> List:
        r"""
        The generation of CPMBee.
        1. It will use beam search as generation strategy.
        2. It will use CpmBeeBeamSearchScorer as the beamsearch scorer.
        """
        if synced_gpus is None:
            if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
                synced_gpus = True
            else:
                synced_gpus = False

        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
        self._validate_model_class()

        # priority: `generation_config` argument > `model.generation_config` (the default generation config)
        if generation_config is None:
            # legacy: users may modify the model configuration to control generation -- update the generation config
            # model attribute accordingly, if it was created from the model config
            if self.generation_config._from_model_config:
                new_generation_config = GenerationConfig.from_model_config(self.config)
                if new_generation_config != self.generation_config:
                    warnings.warn(
                        "You have modified the pretrained model configuration to control generation. This is a"
                        " deprecated strategy to control generation and will be removed soon, in a future version."
                        " Please use a generation configuration file (see"
                        " https://huggingface.co/docs/transformers/main_classes/text_generation)"
                    )
                    self.generation_config = new_generation_config
            generation_config = self.generation_config

        generation_config = copy.deepcopy(generation_config)
        model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
        generation_config.validate()
        self._validate_model_kwargs(model_kwargs.copy())

        # 2. Set generation parameters if not already defined
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()

        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
            if model_kwargs.get("attention_mask", None) is None:
                logger.warning(
                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
                )
            eos_token_id = generation_config.eos_token_id
            if isinstance(eos_token_id, list):
                eos_token_id = eos_token_id[0]
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
            generation_config.pad_token_id = eos_token_id

        # 3. Define model inputs
        # inputs_tensor has to be defined
        # model_input_name is defined if model-specific keyword input is passed
        # otherwise model_input_name is None
        # all model-specific keyword inputs are removed from `model_kwargs`
        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
            inputs, generation_config.bos_token_id, model_kwargs
        )
        batch_size = inputs_tensor.shape[0]

        # 4. Define other model kwargs
        model_kwargs["output_attentions"] = generation_config.output_attentions
        model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
        model_kwargs["use_cache"] = generation_config.use_cache

        accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
        requires_attention_mask = "encoder_outputs" not in model_kwargs

        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
            )

        # decoder-only models should use left-padding for generation
        if not self.config.is_encoder_decoder:
            # If `input_ids` was given, check if the last id in any sequence is `pad_token_id`
            # Note: If using, `inputs_embeds` this check does not work, because we want to be more hands-off.
            if (
                generation_config.pad_token_id is not None
                and len(inputs_tensor.shape) == 2
                and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
            ):
                logger.warning(
                    "A decoder-only architecture is being used, but right-padding was detected! For correct "
                    "generation results, please set `padding_side='left'` when initializing the tokenizer."
                )

        # 5. Prepare `input_ids` which will be used for auto-regressive generation
        input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")

        if streamer is not None:
            streamer.put(input_ids.cpu())

        # 6. Prepare `max_length` depending on other stopping criteria.
        input_ids_seq_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        if has_default_max_length and generation_config.max_new_tokens is None:
            warnings.warn(
                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
                " recommend using `max_new_tokens` to control the maximum length of the generation.",
                UserWarning,
            )
        elif generation_config.max_new_tokens is not None:
            if not has_default_max_length:
                logger.warning(
                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
                    "Please refer to the documentation for more information. "
                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
                )
            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length

        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
            raise ValueError(
                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
                f" the maximum length ({generation_config.max_length})"
            )
        if input_ids_seq_length >= generation_config.max_length:
            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
            logger.warning(
                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
                " increasing `max_new_tokens`."
            )

        if streamer is not None and (generation_config.num_beams > 1):
            raise ValueError(
                "`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
            )

        if self.device.type != input_ids.device.type:
            warnings.warn(
                "You are calling .generate() with the `input_ids` being on a device type different"
                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
                " Please make sure that you have put `input_ids` to the"
                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
                " running `.generate()`.",
                UserWarning,
            )

        # 7. prepare distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_seq_length,
            encoder_input_ids=inputs_tensor,
            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
            logits_processor=logits_processor,
        )

        # 8. prepare beam search scorer
        beam_scorer = CpmBeeBeamSearchScorer(
            batch_size=batch_size,
            num_beams=generation_config.num_beams,
            device=inputs_tensor.device,
            length_penalty=generation_config.length_penalty,
            do_early_stopping=generation_config.early_stopping,
            num_beam_hyps_to_keep=generation_config.num_return_sequences,
            max_length=generation_config.max_new_tokens,
            **kwargs,
        )
        # 9. interleave input_ids with `num_beams` additional sequences per batch
        input_ids, model_kwargs = self._expand_inputs_for_generation(
            input_ids=input_ids,
            expand_size=generation_config.num_beams,
            is_encoder_decoder=self.config.is_encoder_decoder,
            **model_kwargs,
        )
        # 10. run beam search
        return self.beam_search(
            input_ids,
            beam_scorer,
            repetition_penalty=repetition_penalty,
            logits_processor=logits_processor,
            max_length=generation_config.max_new_tokens,
            pad_token_id=generation_config.pad_token_id,
            eos_token_id=generation_config.eos_token_id,
            vocab_size=kwargs.get("vocab_size", None),
            output_scores=generation_config.output_scores,
            return_dict_in_generate=generation_config.return_dict_in_generate,
            synced_gpus=synced_gpus,
            **model_kwargs,
        )

    @torch.no_grad()
    def generate(
        self,
        data_list: Union[Dict, List[Dict]],
        tokenizer: CpmBeeTokenizer,
        **kwargs,
    ):
        """
        Override the generate for CPMBee. It will accept dict or list(dict) as input and returns dict or list(dict)
        with `<ans>` filled.

        Parameters:
            data_list (`dict` or `list(dict)`):
                The sequence used as a prompt for the generation or as model inputs to the encoder. If dict, data_list
                will be wrapped as a list.
            tokenizer: (`CpmBeeTokenizer`):
                The tokenizer.
            generation_config (`~generation.GenerationConfig`, *optional*):
                The generation configuration to be used as base parametrization for the generation call. `**kwargs`
                passed to generate matching the attributes of `generation_config` will override them. If
                `generation_config` is not provided, the default will be used, which had the following loading
                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
                default values, whose documentation should be checked to parameterize generation.
        """
        if isinstance(data_list, dict):
            data_list = [data_list]
        input_encoded = tokenizer(data_list, return_tensors="pt", padding=True, device=self.device)
        input_encoded.update(kwargs)
        input_encoded["vocab_size"] = tokenizer.vocab_size

        decode_res = self._generate(**input_encoded)

        for sent_id, result in enumerate(decode_res):
            ans_result_map: Dict[int, List[int]] = {}
            for raw_word_id, ans_id in result:
                if ans_id not in ans_result_map:
                    ans_result_map[ans_id] = []
                ans_result_map[ans_id].append(raw_word_id)

            answer_placeholders = input_encoded["other_info"][sent_id]["answer_placeholders"]
            ext_table = input_encoded["other_info"][sent_id]["ext_table"]
            data = data_list[sent_id]
            for ans_id, token_ids in ans_result_map.items():
                if token_ids[-1] == tokenizer.eos_token_id:
                    token_ids = token_ids[:-1]
                text = tokenizer.decode(token_ids, ext_table)
                path = answer_placeholders[ans_id - 1]

                if len(path) > 0:
                    p = data["<ans>"]
                    for part in path[:-1]:
                        p = p[part]
                    p[path[-1]] = text
                else:
                    data["<ans>"] = text
            for ans_id in range(len(answer_placeholders)):
                if (ans_id + 1) not in ans_result_map:
                    path = answer_placeholders[ans_id]
                    p = data["<ans>"]
                    for part in path[:-1]:
                        p = p[part]
                    p[path[-1]] = None
        return data_list