File size: 162,906 Bytes
122057f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
"""TF general model utils."""

from __future__ import annotations

import functools
import gc
import inspect
import json
import os
import pickle
import re
import warnings
from collections.abc import Mapping
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union

import h5py
import numpy as np
import tensorflow as tf
from huggingface_hub import Repository, list_repo_files
from keras import backend as K
from packaging.version import parse

from . import DataCollatorWithPadding, DefaultDataCollator
from .activations_tf import get_tf_activation
from .configuration_utils import PretrainedConfig
from .dynamic_module_utils import custom_object_save
from .generation import GenerationConfig, TFGenerationMixin
from .tf_utils import (
    expand_1d,
    load_attributes_from_hdf5_group,
    save_attributes_to_hdf5_group,
    shape_list,
)
from .utils import (
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    TF2_WEIGHTS_INDEX_NAME,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ModelOutput,
    PushToHubMixin,
    cached_file,
    download_url,
    find_labels,
    has_file,
    is_offline_mode,
    is_remote_url,
    is_safetensors_available,
    is_tf_symbolic_tensor,
    logging,
    requires_backends,
    working_or_temp_dir,
)
from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files


if is_safetensors_available():
    from safetensors import safe_open
    from safetensors.tensorflow import save_file as safe_save_file

if TYPE_CHECKING:
    from . import PreTrainedTokenizerBase


logger = logging.get_logger(__name__)
tf_logger = tf.get_logger()

TFModelInputType = Union[
    List[tf.Tensor],
    List[np.ndarray],
    Dict[str, tf.Tensor],
    Dict[str, np.ndarray],
    tf.Tensor,
    np.ndarray,
]


def dummy_loss(y_true, y_pred):
    if y_pred.shape.rank <= 1:
        return y_pred
    else:
        reduction_axes = list(range(1, y_pred.shape.rank))
        return tf.reduce_mean(y_pred, axis=reduction_axes)


class TFModelUtilsMixin:
    """
    A few utilities for `tf.keras.Model`, to be used as a mixin.
    """

    def num_parameters(self, only_trainable: bool = False) -> int:
        """
        Get the number of (optionally, trainable) parameters in the model.

        Args:
            only_trainable (`bool`, *optional*, defaults to `False`):
                Whether or not to return only the number of trainable parameters

        Returns:
            `int`: The number of parameters.
        """
        if only_trainable:
            return int(sum(np.prod(w.shape.as_list()) for w in self.trainable_variables))
        else:
            return self.count_params()


def keras_serializable(cls):
    """
    Decorate a Keras Layer class to support Keras serialization.

    This is done by:

    1. Adding a `transformers_config` dict to the Keras config dictionary in `get_config` (called by Keras at
       serialization time.
    2. Wrapping `__init__` to accept that `transformers_config` dict (passed by Keras at deserialization time) and
       convert it to a config object for the actual layer initializer.
    3. Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does not
       need to be supplied in `custom_objects` in the call to `tf.keras.models.load_model`.

    Args:
        cls (a `tf.keras.layers.Layers subclass`):
            Typically a `TF.MainLayer` class in this project, in general must accept a `config` argument to its
            initializer.

    Returns:
        The same class object, with modifications for Keras deserialization.
    """
    initializer = cls.__init__

    config_class = getattr(cls, "config_class", None)
    if config_class is None:
        raise AttributeError("Must set `config_class` to use @keras_serializable")

    @functools.wraps(initializer)
    def wrapped_init(self, *args, **kwargs):
        config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.pop("config", None)

        if isinstance(config, dict):
            config = config_class.from_dict(config)
            initializer(self, config, *args, **kwargs)
        elif isinstance(config, PretrainedConfig):
            if len(args) > 0:
                initializer(self, *args, **kwargs)
            else:
                initializer(self, config, *args, **kwargs)
        else:
            raise ValueError("Must pass either `config` (PretrainedConfig) or `config` (dict)")

        self._config = config
        self._kwargs = kwargs

    cls.__init__ = wrapped_init

    if not hasattr(cls, "get_config"):
        raise TypeError("Only use @keras_serializable on tf.keras.layers.Layer subclasses")
    if hasattr(cls.get_config, "_is_default"):

        def get_config(self):
            cfg = super(cls, self).get_config()
            cfg["config"] = self._config.to_dict()
            cfg.update(self._kwargs)
            return cfg

        cls.get_config = get_config

    cls._keras_serializable = True
    if hasattr(tf.keras.utils, "register_keras_serializable"):
        cls = tf.keras.utils.register_keras_serializable()(cls)
    return cls


class TFCausalLanguageModelingLoss:
    """
    Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token.

    <Tip>

    Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

    </Tip>
    """

    def hf_compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        if self.config.tf_legacy_loss:
            # make sure only labels that are not equal to -100 affect the loss
            active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100)
            reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
            labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)
            return loss_fn(labels, reduced_logits)

        # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
        unmasked_loss = loss_fn(tf.nn.relu(labels), logits)
        # make sure only labels that are not equal to -100 affect the loss
        loss_mask = tf.cast(labels != -100, dtype=unmasked_loss.dtype)
        masked_loss = unmasked_loss * loss_mask
        reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(loss_mask)
        return tf.reshape(reduced_masked_loss, (1,))


class TFQuestionAnsweringLoss:
    """
    Loss function suitable for question answering.
    """

    def hf_compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        start_loss = loss_fn(labels["start_position"], logits[0])
        end_loss = loss_fn(labels["end_position"], logits[1])

        return (start_loss + end_loss) / 2.0


class TFTokenClassificationLoss:
    """
    Loss function suitable for token classification.

    <Tip>

    Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

    </Tip>
    """

    def hf_compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        if tf.executing_eagerly():  # Data-dependent conditionals are forbidden in XLA
            if tf.math.reduce_any(labels == -1):
                tf.print("Using `-1` to mask the loss for the token is deprecated. Please use `-100` instead.")

        if self.config.tf_legacy_loss:
            # make sure only labels that are not equal to -100
            # are taken into account as loss
            if tf.math.reduce_any(labels == -1):
                tf.print("Using `-1` to mask the loss for the token is deprecated. Please use `-100` instead.")
                active_loss = tf.reshape(labels, (-1,)) != -1
            else:
                active_loss = tf.reshape(labels, (-1,)) != -100
            reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
            labels = tf.boolean_mask(tf.reshape(labels, (-1,)), active_loss)

            return loss_fn(labels, reduced_logits)

        # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
        unmasked_loss = loss_fn(tf.nn.relu(labels), logits)
        # make sure only labels that are not equal to -100 or -1
        # are taken into account as loss
        loss_mask = tf.cast(labels >= 0, dtype=unmasked_loss.dtype)
        # Avoid possible division by zero later
        # Masked positions will have a loss of NaN because -100 and -1 are not valid labels
        masked_loss = unmasked_loss * loss_mask
        reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(loss_mask)
        return tf.reshape(reduced_masked_loss, (1,))


class TFSequenceClassificationLoss:
    """
    Loss function suitable for sequence classification.
    """

    def hf_compute_loss(self, labels, logits):
        if logits.shape.rank == 1 or logits.shape[1] == 1:
            loss_fn = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)
            if labels.shape.rank == 1:
                # MeanSquaredError returns a scalar loss if the labels are 1D, so avoid that
                labels = tf.expand_dims(labels, axis=-1)
        else:
            loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
                from_logits=True, reduction=tf.keras.losses.Reduction.NONE
            )

        return loss_fn(labels, logits)


class TFMultipleChoiceLoss:
    """Loss function suitable for multiple choice tasks."""

    def hf_compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        return loss_fn(labels, logits)


class TFMaskedLanguageModelingLoss(TFCausalLanguageModelingLoss):
    """
    Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens.

    <Tip>

    Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

    </Tip>
    """


class TFNextSentencePredictionLoss:
    """
    Loss function suitable for next sentence prediction (NSP), that is, the task of guessing the next sentence.

    <Tip>

    Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.

    </Tip>
    """

    def hf_compute_loss(self, labels, logits):
        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
        )
        if self.config.tf_legacy_loss:
            # make sure only labels that are not equal to -100
            # are taken into account as loss
            next_sentence_active_loss = tf.not_equal(tf.reshape(labels, (-1,)), -100)
            next_sentence_reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, 2)), next_sentence_active_loss)
            next_sentence_label = tf.boolean_mask(tf.reshape(labels, (-1,)), next_sentence_active_loss)

            return loss_fn(next_sentence_label, next_sentence_reduced_logits)

        # make sure only labels that are not equal to -100
        # are taken into account as loss

        # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
        unmasked_ns_loss = loss_fn(y_true=tf.nn.relu(labels), y_pred=logits)
        ns_loss_mask = tf.cast(labels != -100, dtype=unmasked_ns_loss.dtype)
        # Just zero out samples where label is -100, no reduction
        masked_ns_loss = unmasked_ns_loss * ns_loss_mask

        return masked_ns_loss


def booleans_processing(config, **kwargs):
    """
    Process the input booleans of each model.

    Args:
        config ([`PretrainedConfig`]):
            The config of the running model.
        **kwargs:
            The boolean parameters

    Returns:
        A dictionary with the proper values for each boolean
    """
    final_booleans = {}

    # Pure conv models (such as ConvNext) do not have `output_attentions`. If the signature has
    # `output_attentions`, it will be present here in `kwargs`, even if unset (in that case, as `None`)
    if "output_attentions" in kwargs:
        final_booleans["output_attentions"] = (
            kwargs["output_attentions"] if kwargs["output_attentions"] is not None else config.output_attentions
        )
    final_booleans["output_hidden_states"] = (
        kwargs["output_hidden_states"] if kwargs["output_hidden_states"] is not None else config.output_hidden_states
    )
    final_booleans["return_dict"] = kwargs["return_dict"] if kwargs["return_dict"] is not None else config.return_dict

    if "use_cache" in kwargs:
        final_booleans["use_cache"] = (
            kwargs["use_cache"] if kwargs["use_cache"] is not None else getattr(config, "use_cache", None)
        )
    return final_booleans


def unpack_inputs(func):
    """
    Decorator that processes the inputs to a Keras layer, passing them to the layer as keyword arguments. This enables
    downstream use of the inputs by their variable name, even if they arrive packed as a dictionary in the first input
    (common case in Keras).

    Args:
        func (`callable`):
            The callable function of the TensorFlow model.


    Returns:
        A callable that wraps the original `func` with the behavior described above.
    """

    original_signature = inspect.signature(func)

    @functools.wraps(func)
    def run_call_with_unpacked_inputs(self, *args, **kwargs):
        # isolates the actual `**kwargs` for the decorated function
        kwargs_call = {key: val for key, val in kwargs.items() if key not in dict(original_signature.parameters)}
        fn_args_and_kwargs = {key: val for key, val in kwargs.items() if key not in kwargs_call}
        fn_args_and_kwargs.update({"kwargs_call": kwargs_call})

        # move any arg into kwargs, if they exist
        fn_args_and_kwargs.update(dict(zip(func.__code__.co_varnames[1:], args)))

        # Encoder Decoder models delegate the application of the configuration options to their inner models.
        if "EncoderDecoder" in self.__class__.__name__:
            config = None
        else:
            config = self.config

        unpacked_inputs = input_processing(func, config, **fn_args_and_kwargs)
        return func(self, **unpacked_inputs)

    # Keras enforces the first layer argument to be passed, and checks it through `inspect.getfullargspec()`. This
    # function does not follow wrapper chains (i.e. ignores `functools.wraps()`), meaning that without the line below
    # Keras would attempt to check the first argument against the literal signature of the wrapper.
    run_call_with_unpacked_inputs.__signature__ = original_signature

    return run_call_with_unpacked_inputs


def input_processing(func, config, **kwargs):
    """
    Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input
    has to be named accordingly to the parameters name, i.e. `input_ids = tf.keras.Input(shape=(128,), dtype='int32',
    name="input_ids")` otherwise the order of the tensors will not be guaranteed during the training.

    Args:
        func (`callable`):
            The callable function of the TensorFlow model.
        config ([`PretrainedConfig`]):
            The config of the running model.
        **kwargs:
            The inputs of the model.

    Returns:
        Two lists, one for the missing layers, and another one for the unexpected layers.
    """
    signature = dict(inspect.signature(func).parameters)
    has_kwargs = bool(signature.pop("kwargs", None))
    signature.pop("self", None)
    parameter_names = list(signature.keys())
    main_input_name = parameter_names[0]
    main_input = kwargs.pop(main_input_name, None)
    output = {}
    allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray)

    if "inputs" in kwargs["kwargs_call"]:
        warnings.warn(
            "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.",
            FutureWarning,
        )

        output["input_ids"] = kwargs["kwargs_call"].pop("inputs")

    if "decoder_cached_states" in kwargs["kwargs_call"]:
        warnings.warn(
            "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use"
            " `past_key_values` instead.",
            FutureWarning,
        )
        output["past_key_values"] = kwargs["kwargs_call"].pop("decoder_cached_states")

    if "past" in kwargs["kwargs_call"] and "past_key_values" in parameter_names:
        warnings.warn(
            "The `past` argument is deprecated and will be removed in a future version, use `past_key_values`"
            " instead.",
            FutureWarning,
        )
        kwargs["past_key_values"] = kwargs["kwargs_call"].pop("past")
    elif "past_key_values" in kwargs["kwargs_call"] and "past" in parameter_names:
        kwargs["past"] = kwargs["kwargs_call"].pop("past_key_values")

    if has_kwargs:
        output["kwargs"] = kwargs.pop("kwargs_call", {})
    else:
        if len(kwargs["kwargs_call"]) > 0:
            raise ValueError(
                "The following keyword arguments are not supported by this model:"
                f" {list(kwargs['kwargs_call'].keys())}."
            )
        kwargs.pop("kwargs_call")

    for k, v in kwargs.items():
        if isinstance(v, allowed_types) or tf.is_tensor(v) or v is None:
            output[k] = v
        else:
            raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")

    if isinstance(main_input, (tuple, list)):
        for i, input in enumerate(main_input):
            # EagerTensors don't allow to use the .name property so we check for a real Tensor
            if is_tf_symbolic_tensor(input):
                # Tensor names have always the pattern `name:id` then we check only the
                # `name` part
                tensor_name = input.name.split(":")[0]

                if tensor_name in parameter_names:
                    output[tensor_name] = input
                else:
                    output[parameter_names[i]] = input
            elif isinstance(input, allowed_types) or input is None:
                output[parameter_names[i]] = input
            else:
                raise ValueError(
                    f"Data of type {type(input)} is not allowed only {allowed_types} is accepted for"
                    f" {parameter_names[i]}."
                )
    elif isinstance(main_input, Mapping):
        if "inputs" in main_input:
            warnings.warn(
                "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids`"
                " instead.",
                FutureWarning,
            )

            output["input_ids"] = main_input.pop("inputs")

        if "decoder_cached_states" in main_input:
            warnings.warn(
                "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use"
                " `past_key_values` instead.",
                FutureWarning,
            )
            output["past_key_values"] = main_input.pop("decoder_cached_states")

        for k, v in dict(main_input).items():
            if isinstance(v, allowed_types) or v is None:
                output[k] = v
            elif k not in parameter_names and "args" not in parameter_names:
                logger.warning(
                    f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored."
                )
                continue
            else:
                raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
    else:
        if tf.is_tensor(main_input) or main_input is None:
            output[main_input_name] = main_input
        else:
            raise ValueError(
                f"Data of type {type(main_input)} is not allowed only {allowed_types} is accepted for"
                f" {main_input_name}."
            )

    # Populates any unspecified argument with their default value, according to the signature.
    for name in parameter_names:
        if name not in list(output.keys()) and name != "args":
            output[name] = kwargs.pop(name, signature[name].default)

    # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
    # So to respect the proper output we have to add this exception
    if "args" in output:
        if output["args"] is not None and is_tf_symbolic_tensor(output["args"]):
            tensor_name = output["args"].name.split(":")[0]
            output[tensor_name] = output["args"]
        else:
            # `args` in this case is always the first parameter, then `input_ids`
            output["input_ids"] = output["args"]

        del output["args"]

    if "kwargs" in output:
        del output["kwargs"]

    cast_output = {}
    for key, val in output.items():
        if isinstance(val, tf.Tensor) and val.dtype == tf.int64:
            cast_output[key] = tf.cast(val, tf.int32)
        elif isinstance(val, np.ndarray) and val.dtype == np.int64:
            cast_output[key] = val.astype(np.int32)
        else:
            cast_output[key] = val

    output = cast_output
    del cast_output

    if config is not None:
        boolean_dict = {
            k: v
            for k, v in output.items()
            if k in ["return_dict", "output_attentions", "output_hidden_states", "use_cache"]
        }

        output.update(
            booleans_processing(
                config=config,
                **boolean_dict,
            )
        )

    return output


def dtype_byte_size(dtype):
    """
    Returns the size (in bytes) occupied by one parameter of type `dtype`.

    Example:

    ```py
    >>> dtype_byte_size(tf.float32)
    4
    ```
    """
    if dtype == tf.bool:
        return 1 / 8
    bit_search = re.search(r"[^\d](\d+)$", dtype.name)
    if bit_search is None:
        raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
    bit_size = int(bit_search.groups()[0])
    return bit_size // 8


def strip_model_name_and_prefix(name, _prefix=None):
    if _prefix is not None and name.startswith(_prefix):
        name = name[len(_prefix) :]
        if name.startswith("/"):
            name = name[1:]
    if "model." not in name and len(name.split("/")) > 1:
        name = "/".join(name.split("/")[1:])
    return name


def tf_shard_checkpoint(weights, max_shard_size="10GB"):
    """
    Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a
    given size.

    The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no
    optimization made to make each sub-checkpoint as close as possible to the maximum size passed. For example, if the
    limit is 10GB and we have weights of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB],
    [6+2+2GB] and not [6+2+2GB], [6+2GB], [6GB].

    <Tip warning={true}>

    If one of the model's weight is bigger that `max_shard_size`, it will end up in its own sub-checkpoint which will
    have a size greater than `max_shard_size`.

    </Tip>

    Args:
        weights (`Dict[str, tf.RessourceVariable]`): The list of tf.RessourceVariable of a model to save.
        max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`):
            The maximum size of each sub-checkpoint. If expressed as a string, needs to be digits followed by a unit
            (like `"5MB"`).
    """
    max_shard_size = convert_file_size_to_int(max_shard_size)

    sharded_state_dicts = []
    current_block = []
    current_block_size = 0
    total_size = 0

    for item in weights:
        weight_size = item.numpy().size * dtype_byte_size(item.dtype)

        # If this weight is going to tip up over the maximal size, we split.
        if current_block_size + weight_size > max_shard_size:
            sharded_state_dicts.append(current_block)
            current_block = []
            current_block_size = 0

        current_block.append(item)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    sharded_state_dicts.append(current_block)

    # If we only have one shard, we return it
    if len(sharded_state_dicts) == 1:
        return {TF2_WEIGHTS_NAME: sharded_state_dicts[0]}, None

    # Otherwise, let's build the index
    weight_map = {}
    shards = {}
    for idx, shard in enumerate(sharded_state_dicts):
        shard_file = TF2_WEIGHTS_NAME.replace(".h5", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.h5")
        shards[shard_file] = shard
        for weight in shard:
            weight_name = weight.name
            weight_map[weight_name] = shard_file

    # Add the metadata
    metadata = {"total_size": total_size}
    index = {"metadata": metadata, "weight_map": weight_map}
    return shards, index


def load_tf_sharded_weights(model, shard_files, ignore_mismatched_sizes=False, strict=False, _prefix=None):
    """
    This is the same as `load_tf_weights` but for a sharded checkpoint. Detect missing and unexpected layers and load
    the TF weights from the shard file accordingly to their names and shapes.

    This load is performed efficiently: each checkpoint shard is loaded one by one in RAM and deleted after being
    loaded in the model.

    Args:
        model (`tf.keras.models.Model`): The model in which to load the checkpoint.
        shard_files (`str` or `os.PathLike`): A list containing the sharded checkpoint names.
        ignore_mismatched_sizes`bool`, *optional`, defaults to `True`):
            Whether or not to ignore the mismatch between the sizes
        strict (`bool`, *optional*, defaults to `True`):
            Whether to strictly enforce that the keys in the model state dict match the keys in the sharded checkpoint.

    Returns:
        Three lists, one for the missing layers, another one for the unexpected layers, and a last one for the
        mismatched layers.
    """

    # Load the index
    unexpected_keys = set()
    saved_keys = set()
    mismatched_keys = set()

    # Since TF adds the name of the class to its weights, and uses the index and not the name of the layer to load
    # the weight, we have to get rid of the first prefix of the name of the layer.
    model_keys = set()
    model_layer_map = {}
    for i, k in enumerate(model.weights):
        layer_name = k.name
        if _prefix is not None and layer_name.startswith(_prefix):
            layer_name = layer_name[len(_prefix) :]
            layer_name = layer_name.lstrip("/")
        if not ("model." in layer_name or len(layer_name.split("/")) == 1):
            layer_name = "/".join(layer_name.split("/")[1:])
        model_keys.add(layer_name)
        model_layer_map[layer_name] = i

    for shard_file in shard_files:
        saved_weight_names_set, unexpected_keys_set, mismatched_keys_set = load_tf_shard(
            model,
            model_layer_map,
            shard_file,
            ignore_mismatched_sizes=ignore_mismatched_sizes,
            _prefix=_prefix,
        )
        saved_keys.update(saved_weight_names_set)
        unexpected_keys.update(unexpected_keys_set)
        mismatched_keys.update(mismatched_keys_set)
        gc.collect()

    missing_keys = model_keys - saved_keys
    if strict and (len(missing_keys) > 0 or len(unexpected_keys) > 0):
        error_message = f"Error(s) in loading state_dict for {model.__class__.__name__}"
        if len(missing_keys) > 0:
            str_missing_keys = ",".join([f'"{k}"' for k in missing_keys])
            error_message += f"\nMissing key(s): {str_missing_keys}."
        if len(unexpected_keys) > 0:
            str_unexpected_keys = ",".join([f'"{k}"' for k in unexpected_keys])
            error_message += f"\nMissing key(s): {str_unexpected_keys}."
        raise RuntimeError(error_message)

    return missing_keys, unexpected_keys, mismatched_keys


def load_tf_shard(model, model_layer_map, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
    """
    Loads a shard from a sharded checkpoint file. Handles the missing keys and unexpected keys.

    Args:
        model (`tf.keras.models.Model`): Model in which the weights are loaded
        model_layer_map (`Dict`): A dictionary mapping the layer name to the index of the layer in the model.
        resolved_archive_file (`str`): Path to the checkpoint file from which the weights will be loaded
        ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`): Whether to ignore the mismatched keys

    Returns:
        `tf.keras.models.Model`: Three lists, one for the layers that were found and succesfully restored (from the
        shard file), one for the mismatched layers, and another one for the unexpected layers.
    """
    saved_weight_names_set = set()
    saved_weights = {}
    mismatched_keys = set()
    unexpected_keys = set()
    # Read the H5 file
    try:
        with h5py.File(resolved_archive_file, "r") as sharded_checkpoint_file:
            # Retrieve the name of each layer from the H5 file
            saved_h5_model_layers_name = set(load_attributes_from_hdf5_group(sharded_checkpoint_file, "layer_names"))
            weight_value_tuples = []

            # Compute missing and unexpected sub layers
            # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...]
            for layer_name in saved_h5_model_layers_name:
                h5_layer_object = sharded_checkpoint_file[layer_name]
                saved_weights[layer_name] = np.asarray(h5_layer_object)

                saved_weight_names_set.add(layer_name)

                if layer_name not in model_layer_map:
                    unexpected_keys.add(layer_name)
                else:
                    symbolic_weight = model.weights[model_layer_map[layer_name]]

                    saved_weight_value = saved_weights[layer_name]
                    # If the current weight is found
                    if saved_weight_value is not None:
                        # Check if the shape of the current weight and the one from the H5 file are different
                        if K.int_shape(symbolic_weight) != saved_weight_value.shape:
                            # If yes we reshape the weight from the H5 file accordingly to the current weight
                            # If the two shapes are not compatible we raise an issue
                            try:
                                array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight))
                            except ValueError as e:
                                if ignore_mismatched_sizes:
                                    mismatched_keys.add(
                                        (layer_name, saved_weight_value.shape, K.int_shape(symbolic_weight))
                                    )
                                    continue
                                else:
                                    raise e
                        else:
                            array = saved_weight_value

                    # We create the tuple that will be loaded and add it to the final list
                    weight_value_tuples.append((symbolic_weight, array))

        K.batch_set_value(weight_value_tuples)

        return saved_weight_names_set, unexpected_keys, mismatched_keys

    except Exception as e:
        try:
            with open(resolved_archive_file) as f:
                if f.read().startswith("version"):
                    raise OSError(
                        "You seem to have cloned a repository without having git-lfs installed. Please install "
                        "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
                        "you cloned."
                    )
                else:
                    raise ValueError(
                        f"Unable to locate the file {resolved_archive_file} which is necessary to load this pretrained"
                        " model. Make sure you have saved the model properly."
                    ) from e
        except (UnicodeDecodeError, ValueError):
            raise OSError(
                f"Unable to load weights from TF checkpoint file for '{resolved_archive_file}' "
                f"at '{resolved_archive_file}'. "
                "If you tried to load a TF model from a sharded checkpoint, you should try converting the model "
                "by loading it in pytorch and saving it localy. A convertion script should be realeased soon."
            )


def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
    """
    Detect missing and unexpected layers and load the TF weights from the shard file accordingly to their names and
    shapes.

    Args:
        model (`tf.keras.models.Model`):
            The model to load the weights into.
        resolved_archive_file (`str`):
            The location of the H5 file.
        ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
            Whether or not to ignore weights with shapes that don't match between the checkpoint of the model.

    Returns:
        Three lists, one for the missing layers, another one for the unexpected layers, and a last one for the
        mismatched layers.
    """
    if resolved_archive_file.endswith(".safetensors"):
        load_function = load_tf_weights_from_safetensors
    else:
        load_function = load_tf_weights_from_h5

    return load_function(
        model, resolved_archive_file, ignore_mismatched_sizes=ignore_mismatched_sizes, _prefix=_prefix
    )


def load_tf_weights_from_h5(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
    mismatched_layers = []

    # Read the H5 file
    with h5py.File(resolved_archive_file, "r") as sharded_checkpoint_file:
        # Retrieve the name of each layer from the H5 file
        saved_h5_model_layers_name = set(load_attributes_from_hdf5_group(sharded_checkpoint_file, "layer_names"))

        # Find the missing layers from the high level list of layers
        missing_layers = list({layer.name for layer in model.layers} - saved_h5_model_layers_name)

        # Find the unexpected layers from the high level list of layers
        unexpected_layers = list(saved_h5_model_layers_name - {layer.name for layer in model.layers})
        saved_weight_names_set = set()
        symbolic_weights_names = set()
        weight_value_tuples = []

        # Compute missing and unexpected sub layers
        # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...]
        for layer in model.layers:
            # if layer_name from the H5 file belongs to the layers from the instantiated model
            if layer.name in saved_h5_model_layers_name:
                # Get the H5 layer object from its name
                h5_layer_object = sharded_checkpoint_file[layer.name]
                # Get all the weights as a list from the layer object
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
                saved_weights = {}

                # Create a dict from the H5 saved model that looks like {"weight_name": weight_value}
                # And a set with only the names
                for weight_name in load_attributes_from_hdf5_group(h5_layer_object, "weight_names"):
                    # TF names always start with the model name so we ignore it
                    name = "/".join(weight_name.split("/")[1:])

                    if _prefix is not None:
                        name = _prefix + "/" + name

                    saved_weights[name] = np.asarray(h5_layer_object[weight_name])

                    # Add the updated name to the final list for computing missing/unexpected values
                    saved_weight_names_set.add(name)

                # Loop over each weights from the instantiated model and compare with the weights from the H5 file
                for symbolic_weight in symbolic_weights:
                    # TF names always start with the model name so we ignore it
                    if _prefix is not None:
                        delimeter = len(_prefix.split("/"))
                        symbolic_weight_name = "/".join(
                            symbolic_weight.name.split("/")[:delimeter]
                            + symbolic_weight.name.split("/")[delimeter + 1 :]
                        )
                    else:
                        symbolic_weight_name = "/".join(symbolic_weight.name.split("/")[1:])

                    # here we check if the current weight is among the weights from the H5 file
                    # If yes, get the weight_value of the corresponding weight from the H5 file
                    # If not, make the value to None
                    saved_weight_value = saved_weights.get(symbolic_weight_name, None)

                    # Retrocompatibility patch: some embeddings are stored with the weights name (e.g. Bart's
                    # `model.shared/embeddings:0` are stored as `model.shared/weights:0`)
                    if saved_weight_value is None and symbolic_weight_name.endswith("embeddings:0"):
                        symbolic_weight_name = symbolic_weight_name[:-12] + "weight:0"
                        saved_weight_value = saved_weights.get(symbolic_weight_name, None)

                    # Add the updated name to the final list for computing missing/unexpected values
                    symbolic_weights_names.add(symbolic_weight_name)

                    # If the current weight is found
                    if saved_weight_value is not None:
                        # Check if the shape of the current weight and the one from the H5 file are different
                        if K.int_shape(symbolic_weight) != saved_weight_value.shape:
                            # If yes we reshape the weight from the H5 file accordingly to the current weight
                            # If the two shapes are not compatible we raise an issue
                            try:
                                array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight))
                            except ValueError as e:
                                if ignore_mismatched_sizes:
                                    mismatched_layers.append(
                                        (symbolic_weight_name, saved_weight_value.shape, K.int_shape(symbolic_weight))
                                    )
                                    continue
                                else:
                                    raise e
                        else:
                            array = saved_weight_value

                        # We create the tuple that will be loaded and add it to the final list
                        weight_value_tuples.append((symbolic_weight, array))

    # Load all the weights
    K.batch_set_value(weight_value_tuples)

    # Compute the missing and unexpected layers
    missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set))
    unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names))

    return missing_layers, unexpected_layers, mismatched_layers


def load_tf_weights_from_safetensors(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None):
    # Read the safetensors file
    with safe_open(resolved_archive_file, framework="tf") as safetensors_archive:
        mismatched_layers = []
        weight_names = [strip_model_name_and_prefix(w.name, _prefix=_prefix) for w in model.weights]
        loaded_weight_names = list(safetensors_archive.keys())
        # Find the missing layers from the high level list of layers
        missing_layers = list(set(weight_names) - set(loaded_weight_names))
        # Find the unexpected layers from the high level list of layers
        unexpected_layers = list(set(loaded_weight_names) - set(weight_names))

        for weight in model.weights:
            weight_name = strip_model_name_and_prefix(weight.name, _prefix=_prefix)
            if weight_name in loaded_weight_names:
                weight_value = safetensors_archive.get_tensor(weight_name)
                # Check if the shape of the current weight and the one from the H5 file are different
                if K.int_shape(weight) != weight_value.shape:
                    # If yes we reshape the weight from the H5 file accordingly to the current weight
                    # If the two shapes are not compatible we raise an issue
                    try:
                        weight_value = tf.reshape(weight_value, K.int_shape(weight))
                    except (ValueError, tf.errors.InvalidArgumentError) as e:
                        if ignore_mismatched_sizes:
                            mismatched_layers.append((weight_name, weight_value.shape, K.int_shape(weight)))
                            continue
                        else:
                            raise e

                K.set_value(weight, weight_value)  # weight.assign() might break if weight is a DTensor
    return missing_layers, unexpected_layers, mismatched_layers


def init_copy_embeddings(old_embeddings, new_num_tokens):
    r"""
    This function aims to reduce the embeddings in case new_num_tokens < old_num_tokens or to pad with -1 in case
    new_num_tokens > old_num_tokens. A mask is also computed in order to know which weight in the embeddings should be
    kept or not. Example:

        - if new_num_tokens=5 and old_num_tokens=4 and old_embeddings=[w1,w2,w3,w4]

            -  mask=[True,True,True,True,False] and current_weights=[w1,w2,w3,w4,-1]
        - if new_num_tokens=4 and old_num_tokens=5 and old_embeddings=[w1,w2,w3,w4,w5]

            - mask=[True,True,True,True] and current_weights=[w1,w2,w3,w4]
    """
    old_num_tokens, old_embedding_dim = shape_list(old_embeddings)
    size_diff = new_num_tokens - old_num_tokens

    # initialize new embeddings
    # Copy token embeddings from the previous ones
    if tf.math.greater(size_diff, 0):
        # if the new size is greater than the old one, we extend the current embeddings with a padding until getting new size
        # and we create a mask to properly identify the padded values and be replaced by the values of the newly created
        # embeddings
        current_weights = tf.pad(
            old_embeddings.value(), tf.convert_to_tensor([[0, size_diff], [0, 0]]), constant_values=-1
        )
        num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
        mask = tf.fill(tf.convert_to_tensor([num_tokens_to_copy, 1]), True)
        mask = tf.pad(mask, tf.convert_to_tensor([[0, size_diff], [0, 0]]), constant_values=False)
    else:
        # if the new size if lower than the old one, we take the current embeddings until the new size
        current_weights = tf.slice(
            old_embeddings.value(),
            tf.convert_to_tensor([0, 0]),
            tf.convert_to_tensor([new_num_tokens, old_embedding_dim]),
        )
        mask = tf.fill(tf.convert_to_tensor([new_num_tokens, 1]), True)

    return mask, current_weights


class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushToHubMixin):
    r"""
    Base class for all TF models.

    [`TFPreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
    downloading and saving models as well as a few methods common to all models to:

        - resize the input embeddings,
        - prune heads in the self-attention heads.

    Class attributes (overridden by derived classes):

        - **config_class** ([`PretrainedConfig`]) -- A subclass of [`PretrainedConfig`] to use as configuration class
          for this model architecture.
        - **base_model_prefix** (`str`) -- A string indicating the attribute associated to the base model in derived
          classes of the same architecture adding modules on top of the base model.
        - **main_input_name** (`str`) -- The name of the principal input to the model (often `input_ids` for NLP
          models, `pixel_values` for vision models and `input_values` for speech models).
    """

    config_class = None
    base_model_prefix = ""
    main_input_name = "input_ids"
    _auto_class = None
    _using_dummy_loss = None
    _label_to_output_map = None

    # a list of re pattern of tensor names to ignore from the model when loading the model weights
    # (and avoid unnecessary warnings).
    _keys_to_ignore_on_load_missing = None
    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
    # (and avoid unnecessary warnings).
    _keys_to_ignore_on_load_unexpected = None
    _requires_load_weight_prefix = False

    @property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
        """
        Dummy inputs to build the network.

        Returns:
            `Dict[str, tf.Tensor]`: The dummy inputs.
        """
        dummies = {}
        for key, spec in self.input_signature.items():
            # 2 is the most correct arbitrary size. I will not be taking questions
            dummy_shape = [dim if dim is not None else 2 for dim in spec.shape]
            if spec.shape[0] is None:
                # But let's make the batch size 1 to save memory anyway
                dummy_shape[0] = 1
            dummies[key] = tf.ones(shape=dummy_shape, dtype=spec.dtype)
            if key == "token_type_ids":
                # Some models have token_type_ids but with a vocab_size of 1
                dummies[key] = tf.zeros_like(dummies[key])
        if self.config.add_cross_attention and "encoder_hidden_states" in inspect.signature(self.call).parameters:
            if "encoder_hidden_states" not in dummies:
                if self.main_input_name == "input_ids":
                    dummies["encoder_hidden_states"] = tf.ones(
                        shape=(1, 2, self.config.hidden_size), dtype=tf.float32, name="encoder_hidden_states"
                    )
                else:
                    raise NotImplementedError(
                        "Model has cross-attention but we couldn't infer the shape for the encoder hidden states. Please manually override dummy_inputs!"
                    )
        return dummies

    def build_in_name_scope(self):
        with tf.name_scope(self.name):
            self.build(input_shape=None)

    @property
    def framework(self) -> str:
        """
        :str: Identifies that this is a TensorFlow model.
        """
        return "tf"

    def build(self, input_shape=None):
        pass  # This is just here to make sure we don't call the superclass build()

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)
        if not isinstance(config, PretrainedConfig):
            raise ValueError(
                f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class "
                "`PretrainedConfig`. To create a model from a pretrained model use "
                f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`"
            )
        # Save config and origin of the pretrained weights if given in model
        self.config = config
        self.name_or_path = config.name_or_path
        self.generation_config = GenerationConfig.from_model_config(config) if self.can_generate() else None
        self._set_save_spec(self.input_signature)

    def get_config(self):
        return self.config.to_dict()

    @classmethod
    def from_config(cls, config, **kwargs):
        if isinstance(config, PretrainedConfig):
            return cls._from_config(config, **kwargs)
        return cls._from_config(cls.config_class.from_dict(config, **kwargs))

    @classmethod
    def _from_config(cls, config, **kwargs):
        """
        All context managers that the model should be initialized under go here.
        """
        return cls(config, **kwargs)

    def get_head_mask(self, head_mask: tf.Tensor | None, num_hidden_layers: int) -> tf.Tensor:
        """
        Prepare the head mask if needed.

        Args:
            head_mask (`tf.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*):
                The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
            num_hidden_layers (`int`):
                The number of hidden layers in the model.

        Returns:
            `tf.Tensor` with shape `[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or list with
            `[None]` for each layer.
        """
        if head_mask is not None:
            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
        else:
            head_mask = [None] * num_hidden_layers

        return head_mask

    def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
        """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
        if head_mask.shape.rank == 1:
            head_mask = head_mask[None, None, :, None, None]
            head_mask = tf.repeat(head_mask, repeats=num_hidden_layers, axis=0)
        elif head_mask.shape.rank == 2:
            head_mask = head_mask[:, None, :, None, None]
        assert head_mask.shape.rank == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
        head_mask = tf.cast(head_mask, tf.float32)  # switch to float if need + fp16 compatibility
        return head_mask

    @tf.function
    def serving(self, inputs):
        """
        Args:
        Method used for serving the model. Does not have a specific signature, but will be specialized as concrete
        functions when saving with `save_pretrained`.
            inputs (`Dict[str, tf.Tensor]`):
                The input of the saved model as a dictionary of tensors.
        """
        output = self.call(inputs)

        return self.serving_output(output)

    def eager_serving(self, inputs):
        """
        Method used for serving the model. This method is deprecated, and will be removed.

        Args:
            inputs (`Dict[str, tf.Tensor]`):
                The input of the saved model as a dictionary of tensors.
        """
        warnings.warn(
            "The function `eager_serving` is deprecated and will be removed in version 4.32.0 of Transformers",
            FutureWarning,
        )
        output = self.call(inputs)

        return self.serving_output(output)

    @property
    def input_signature(self) -> Dict[str, tf.TensorSpec]:
        """
        This property should return a dict mapping input names to tf.TensorSpec objects, representing the expected
        shape and dtype for model inputs. It is used for both serving and for generating the dummy inputs used to build
        the model.
        """
        model_inputs = list(inspect.signature(self.call).parameters)
        sig = {}
        if "input_ids" in model_inputs:
            if self.__class__.__name__.endswith("ForMultipleChoice"):
                text_dims = 3
            else:
                text_dims = 2
            for input_name in (
                "input_ids",
                "attention_mask",
                "token_type_ids",
                "decoder_input_ids",
                "decoder_attention_mask",
            ):
                if input_name in model_inputs:
                    sig[input_name] = tf.TensorSpec([None] * text_dims, tf.int32, name=input_name)
        if "pixel_values" in model_inputs:
            pixel_values_shape = [None, None, None, None]
            if hasattr(self.config, "vision_config"):
                vision_config = self.config.vision_config
            else:
                vision_config = self.config
            if hasattr(vision_config, "num_channels"):
                pixel_values_shape[1] = vision_config.num_channels
            else:
                raise NotImplementedError(
                    "Could not infer number of channels from config, please override input_signature to specify input shapes."
                )
            if hasattr(vision_config, "image_size"):
                pixel_values_shape[2] = pixel_values_shape[3] = vision_config.image_size
            elif hasattr(vision_config, "input_size"):
                pixel_values_shape[2] = pixel_values_shape[3] = vision_config.input_size
            else:
                raise NotImplementedError(
                    "Could not infer input image shape from config, please override input_signature to specify input shapes."
                )
            sig["pixel_values"] = tf.TensorSpec(pixel_values_shape, tf.float32, name="pixel_values")
        if "input_features" in model_inputs:
            raise NotImplementedError("Audio models need a manually defined input_signature")
        return sig

    def serving_output(self, output):
        """
        Prepare the output of the saved model. Can be overridden if specific serving modifications are required.
        """
        if not isinstance(output, ModelOutput):
            return output
        for key in output:
            if key.endswith("hidden_states") and not getattr(self.config, "output_hidden_states", False):
                output[key] = None
            elif key.endswith("attentions") and not getattr(self.config, "output_attentions", False):
                output[key] = None
            elif key == "past_key_values" and not getattr(self.config, "use_cache", False):
                output[key] = None
            elif key == "cross_attentions" and not (
                getattr(self.config, "output_attentions", False) and getattr(self.config, "add_cross_attention", False)
            ):
                output[key] = None
            if isinstance(output[key], (tuple, list)):
                try:
                    output[key] = tf.convert_to_tensor(output[key])
                except (ValueError, tf.errors.InvalidArgumentError):
                    pass  # Layers may not have the same dimensions
        return output

    @classmethod
    def can_generate(cls) -> bool:
        """
        Returns whether this model can generate sequences with `.generate()`.

        Returns:
            `bool`: Whether this model can generate sequences with `.generate()`.
        """
        # Detects whether `prepare_inputs_for_generation` has been overwritten, which is a requirement for generation.
        # Alternativelly, the model can also have a custom `generate` function.
        if "GenerationMixin" in str(cls.prepare_inputs_for_generation) and "GenerationMixin" in str(cls.generate):
            return False
        return True

    def get_input_embeddings(self) -> tf.keras.layers.Layer:
        """
        Returns the model's input embeddings layer.

        Returns:
            `tf.Variable`: The embeddings layer mapping vocabulary to hidden states.
        """
        main_layer = getattr(self, self.base_model_prefix, self)

        if main_layer is not self:
            return main_layer.get_input_embeddings()
        else:
            raise NotImplementedError

    def _save_checkpoint(self, checkpoint_dir, epoch):
        if not os.path.isdir(checkpoint_dir):
            os.mkdir(checkpoint_dir)
        # We avoid tf.train.checkpoint or saving weights in TF format, even though that includes optimizer
        # state for us, because it requires special handling for objects like custom losses, which we use
        # internally and which users are likely to use too
        weights_path = os.path.join(checkpoint_dir, "weights.h5")
        self.save_weights(weights_path)
        extra_data = {"epoch": epoch, "optimizer_state": self.optimizer.get_weights()}
        extra_data_path = os.path.join(checkpoint_dir, "extra_data.pickle")
        with open(extra_data_path, "wb") as f:
            pickle.dump(extra_data, f)

    def load_repo_checkpoint(self, repo_path_or_name):
        """
        Loads a saved checkpoint (model weights and optimizer state) from a repo. Returns the current epoch count when
        the checkpoint was made.

        Args:
            repo_path_or_name (`str`):
                Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case
                the repository will have the name of that local folder).

        Returns:
            `dict`: A dictionary of extra metadata from the checkpoint, most commonly an "epoch" count.
        """
        if getattr(self, "optimizer", None) is None:
            raise RuntimeError(
                "Checkpoint loading failed as no optimizer is attached to the model. "
                "This is most likely caused by the model not being compiled."
            )
        if os.path.isdir(repo_path_or_name):
            local_dir = repo_path_or_name
        else:
            # If this isn't a local path, check that the remote repo exists and has a checkpoint in it
            repo_files = list_repo_files(repo_path_or_name)
            for file in ("checkpoint/weights.h5", "checkpoint/extra_data.pickle"):
                if file not in repo_files:
                    raise FileNotFoundError(f"Repo {repo_path_or_name} does not contain checkpoint file {file}!")
            repo = Repository(repo_path_or_name.split("/")[-1], clone_from=repo_path_or_name)
            local_dir = repo.local_dir

        # Now make sure the repo actually has a checkpoint in it.
        checkpoint_dir = os.path.join(local_dir, "checkpoint")
        weights_file = os.path.join(checkpoint_dir, "weights.h5")
        if not os.path.isfile(weights_file):
            raise FileNotFoundError(f"Could not find checkpoint file weights.h5 in repo {repo_path_or_name}!")
        extra_data_file = os.path.join(checkpoint_dir, "extra_data.pickle")
        if not os.path.isfile(extra_data_file):
            raise FileNotFoundError(f"Could not find checkpoint file extra_data.pickle in repo {repo_path_or_name}!")

        # Assuming the repo is real and we got a checkpoint, load the weights and the optimizer state into the model.
        # The optimizer state includes the iteration count, so learning rate schedules should resume as normal too.
        self.load_weights(weights_file)
        with open(extra_data_file, "rb") as f:
            extra_data = pickle.load(f)
        self.optimizer.set_weights(extra_data["optimizer_state"])

        # Finally, return the epoch number from the checkpoint. This isn't a property of the model, so we can't
        # set it directly, but the user can pass it to fit().
        return {"epoch": extra_data["epoch"]}

    def prepare_tf_dataset(
        self,
        dataset: "datasets.Dataset",  # noqa:F821
        batch_size: int = 8,
        shuffle: bool = True,
        tokenizer: Optional["PreTrainedTokenizerBase"] = None,
        collate_fn: Optional[Callable] = None,
        collate_fn_args: Optional[Dict[str, Any]] = None,
        drop_remainder: Optional[bool] = None,
        prefetch: bool = True,
    ):
        """
        Wraps a HuggingFace [`~datasets.Dataset`] as a `tf.data.Dataset` with collation and batching. This method is
        designed to create a "ready-to-use" dataset that can be passed directly to Keras methods like `fit()` without
        further modification. The method will drop columns from the dataset if they don't match input names for the
        model. If you want to specify the column names to return rather than using the names that match this model, we
        recommend using `Dataset.to_tf_dataset()` instead.

        Args:
            dataset (`Any`):
                A [~`datasets.Dataset`] to be wrapped as a `tf.data.Dataset`.
            batch_size (`int`, defaults to 8):
                The size of batches to return.
            shuffle (`bool`, defaults to `True`):
                Whether to return samples from the dataset in random order. Usually `True` for training datasets and
                `False` for validation/test datasets.
            tokenizer ([`PreTrainedTokenizerBase`], *optional*):
                A `PreTrainedTokenizer` that will be used to pad samples to create batches. Has no effect if a specific
                `collate_fn` is passed instead.
            collate_fn (`Callable`, *optional*):
                A function that collates samples from the dataset into a single batch. Defaults to
                `DefaultDataCollator` if no `tokenizer` is supplied or `DataCollatorWithPadding` if a `tokenizer` is
                passed.
            collate_fn_args (`Dict[str, Any]`, *optional*):
                A dict of arguments to pass to the `collate_fn` alongside the list of samples.
            drop_remainder (`bool`, *optional*):
                Whether to drop the final batch, if the batch_size does not evenly divide the dataset length. Defaults
                to the same setting as `shuffle`.
            prefetch (`bool`, defaults to `True`):
                Whether to add prefetching to the end of the `tf.data` pipeline. This is almost always beneficial for
                performance, but can be disabled in edge cases.


        Returns:
            `Dataset`: A `tf.data.Dataset` which is ready to pass to the Keras API.
        """
        requires_backends(self, ["datasets"])
        import datasets

        if collate_fn is None:
            if tokenizer is None:
                collate_fn = DefaultDataCollator(return_tensors="np")
            else:
                collate_fn = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="np")
        if collate_fn_args is None:
            collate_fn_args = {}

        if not isinstance(dataset, datasets.Dataset):
            raise TypeError("Dataset argument should be a datasets.Dataset!")
        model_inputs = list(inspect.signature(self.call).parameters)
        model_labels = find_labels(self.__class__)
        if "cols_to_retain" in list(inspect.signature(dataset._get_output_signature).parameters.keys()):
            output_signature, _ = dataset._get_output_signature(
                dataset,
                batch_size=None,
                collate_fn=collate_fn,
                collate_fn_args=collate_fn_args,
                cols_to_retain=model_inputs,
            )
        else:
            # TODO Matt: This is a workaround for older versions of datasets that are missing the `cols_to_retain`
            #            argument. We should remove this once the minimum supported version of datasets is > 2.3.2
            unwanted_columns = [
                feature
                for feature in dataset.features
                if feature not in model_inputs and feature not in ("label_ids", "label")
            ]
            dataset = dataset.remove_columns(unwanted_columns)
            output_signature, _ = dataset._get_output_signature(
                dataset, batch_size=None, collate_fn=collate_fn, collate_fn_args=collate_fn_args
            )
        output_columns = list(output_signature.keys())
        feature_cols = [col for col in output_columns if col in model_inputs and col not in model_labels]
        label_cols = [col for col in output_columns if col in model_labels]

        # Backwards compatibility for older versions of datasets. Previously, if `columns` or `label_cols`
        # were a single element list, the returned element spec would be a single element. Now, passing [feature]
        # will return a dict structure {"feature": feature}, and passing a single string will return a single element.
        feature_cols = feature_cols[0] if len(feature_cols) == 1 else feature_cols
        label_cols = label_cols[0] if len(label_cols) == 1 else label_cols

        if drop_remainder is None:
            drop_remainder = shuffle
        tf_dataset = dataset.to_tf_dataset(
            columns=feature_cols,
            label_cols=label_cols,
            batch_size=batch_size,
            shuffle=shuffle,
            drop_remainder=drop_remainder,
            collate_fn=collate_fn,
            collate_fn_args=collate_fn_args,
            prefetch=prefetch,
        )
        return tf_dataset

    def compile(
        self,
        optimizer="rmsprop",
        loss="auto_with_warning",
        metrics=None,
        loss_weights=None,
        weighted_metrics=None,
        run_eagerly=None,
        steps_per_execution=None,
        **kwargs,
    ):
        """
        This is a thin wrapper that sets the model's loss output head as the loss if the user does not specify a loss
        function themselves.
        """
        if loss in ("auto_with_warning", "passthrough"):  # "passthrough" for workflow backward compatibility
            logger.info(
                "No loss specified in compile() - the model's internal loss computation will be used as the "
                "loss. Don't panic - this is a common way to train TensorFlow models in Transformers! "
                "To disable this behaviour please pass a loss argument, or explicitly pass "
                "`loss=None` if you do not want your model to compute a loss. You can also specify `loss='auto'` to "
                "get the internal loss without printing this info string."
            )
            loss = "auto"
        if loss == "auto":
            loss = dummy_loss
            self._using_dummy_loss = True
        else:
            self._using_dummy_loss = False
        parent_args = list(inspect.signature(tf.keras.Model.compile).parameters.keys())
        # This argument got renamed, we need to support both versions
        if "steps_per_execution" in parent_args:
            super().compile(
                optimizer=optimizer,
                loss=loss,
                metrics=metrics,
                loss_weights=loss_weights,
                weighted_metrics=weighted_metrics,
                run_eagerly=run_eagerly,
                steps_per_execution=steps_per_execution,
                **kwargs,
            )
        else:
            super().compile(
                optimizer=optimizer,
                loss=loss,
                metrics=metrics,
                loss_weights=loss_weights,
                weighted_metrics=weighted_metrics,
                run_eagerly=run_eagerly,
                experimental_steps_per_execution=steps_per_execution,
                **kwargs,
            )

    def compute_loss(self, *args, **kwargs):
        if hasattr(tf.keras.Model, "compute_loss"):
            # This will be true in TF 2.8 or greater
            return super().compute_loss(*args, **kwargs)
        else:
            warnings.warn(
                "The old compute_loss method is deprecated as it conflicts with the Keras compute_loss "
                "method added in TF 2.8. If you want the original HF compute_loss, please call "
                "hf_compute_loss() instead. From TF versions >= 2.8, or Transformers versions >= 5, "
                "calling compute_loss() will get the Keras method instead.",
                FutureWarning,
            )
            return self.hf_compute_loss(*args, **kwargs)

    def get_label_to_output_name_mapping(self):
        arg_names = list(inspect.signature(self.call).parameters)
        if self._label_to_output_map is not None:
            return self._label_to_output_map
        elif "start_positions" in arg_names:
            return {"start_positions": "start_logits", "end_positions": "end_logits"}
        elif "sentence_order_label" in arg_names:
            return {"labels": "prediction_logits", "sentence_order_label": "sop_logits"}
        elif "next_sentence_label" in arg_names:
            return {"labels": "prediction_logits", "next_sentence_label": "seq_relationship_logits"}
        elif "mc_labels" in arg_names:
            return {"labels": "logits", "mc_labels": "mc_logits"}
        else:
            return {}

    def train_step(self, data):
        """
        A modification of Keras's default `train_step` that correctly handles matching outputs to labels for our models
        and supports directly training on the loss output head. In addition, it ensures input keys are copied to the
        labels where appropriate. It will also copy label keys into the input dict when using the dummy loss, to ensure
        that they are available to the model during the forward pass.
        """

        # We hardcode the most common renamings; models with weirder names can set `self._label_to_output_map`
        arg_names = list(inspect.signature(self.call).parameters)
        label_kwargs = find_labels(self.__class__)
        label_to_output = self.get_label_to_output_name_mapping()
        output_to_label = {val: key for key, val in label_to_output.items()}
        if not self._using_dummy_loss and parse(tf.__version__) < parse("2.11.0"):
            # Newer TF train steps leave this out
            data = expand_1d(data)
        x, y, sample_weight = tf.keras.utils.unpack_x_y_sample_weight(data)
        # If the inputs are mutable dictionaries, make a shallow copy of them because we will modify
        # them during input/label pre-processing. This avoids surprising the user by wrecking their data.
        # In addition, modifying mutable Python inputs makes XLA compilation impossible.
        if isinstance(x, dict):
            x = x.copy()
        if isinstance(y, dict):
            y = y.copy()

        # When using a dummy loss, we ensure that separate labels are copied to the correct model arguments,
        # if those keys are not already present in the input dict
        if self._using_dummy_loss and y is not None:
            # If y is a tensor and the model only has one label-like input, map y to that input
            if len(label_kwargs) == 1 and isinstance(y, tf.Tensor):
                if isinstance(x, tf.Tensor):
                    x = {arg_names[0]: x}
                label_kwarg = next(iter(label_kwargs))
                if label_kwarg not in x:
                    x[label_kwarg] = y
            # Otherwise, copy keys from y to x as long as they weren't already present in x
            elif isinstance(y, dict):
                if isinstance(x, tf.Tensor):
                    x = {arg_names[0]: x}
                for key, val in y.items():
                    if key in arg_names and key not in x:
                        x[key] = val
                    elif output_to_label.get(key, None) in arg_names and key not in x:
                        x[output_to_label[key]] = val
        if y is None:
            y = {key: val for key, val in x.items() if key in label_kwargs}
            if not y and not self._using_dummy_loss:
                raise ValueError("Could not find label column(s) in input dict and no separate labels were provided!")

        if isinstance(y, dict):
            # Rename labels at this point to match output heads
            y = {label_to_output.get(key, key): val for key, val in y.items()}

        # Run forward pass.
        with tf.GradientTape() as tape:
            if self._using_dummy_loss and "return_loss" in arg_names:
                y_pred = self(x, training=True, return_loss=True)
            else:
                y_pred = self(x, training=True)
            if self._using_dummy_loss:
                loss = self.compiled_loss(y_pred.loss, y_pred.loss, sample_weight, regularization_losses=self.losses)
            else:
                loss = None

            # This next block matches outputs to label keys. Tensorflow's standard method for doing this
            # can get very confused if any of the keys contain nested values (e.g. lists/tuples of Tensors)
            if isinstance(y, dict) and len(y) == 1:
                if list(y.keys())[0] in y_pred.keys():
                    y_pred = y_pred[list(y.keys())[0]]
                elif list(y_pred.keys())[0] == "loss":
                    y_pred = y_pred[1]
                else:
                    y_pred = y_pred[0]
                _, y = y.popitem()
            elif isinstance(y, dict):
                # If the labels are a dict, match keys from the output by name
                y_pred = {key: val for key, val in y_pred.items() if key in y}
            elif isinstance(y, tuple) or isinstance(y, list):
                # If the labels are a tuple/list, match keys to the output by order, skipping the loss.
                if list(y_pred.keys())[0] == "loss":
                    y_pred = y_pred.to_tuple()[1:]
                else:
                    y_pred = y_pred.to_tuple()
                y_pred = y_pred[: len(y)]  # Remove unused fields in case those cause problems
            else:
                # If the labels are a single tensor, match them to the first non-loss tensor in the output
                if list(y_pred.keys())[0] == "loss":
                    y_pred = y_pred[1]
                else:
                    y_pred = y_pred[0]

            if loss is None:
                loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)

        # Run backwards pass.
        self.optimizer.minimize(loss, self.trainable_variables, tape=tape)

        self.compiled_metrics.update_state(y, y_pred, sample_weight)
        # Collect metrics to return
        return_metrics = {}
        for metric in self.metrics:
            result = metric.result()
            if isinstance(result, dict):
                return_metrics.update(result)
            else:
                return_metrics[metric.name] = result
        return return_metrics

    def test_step(self, data):
        """
        A modification of Keras's default `train_step` that correctly handles matching outputs to labels for our models
        and supports directly training on the loss output head. In addition, it ensures input keys are copied to the
        labels where appropriate. It will also copy label keys into the input dict when using the dummy loss, to ensure
        that they are available to the model during the forward pass.
        """
        # We hardcode the most common renamings; models with weirder names can set `self._label_to_output_map`
        arg_names = list(inspect.signature(self.call).parameters)
        label_kwargs = find_labels(self.__class__)
        label_to_output = self.get_label_to_output_name_mapping()
        output_to_label = {val: key for key, val in label_to_output.items()}
        if not self._using_dummy_loss and parse(tf.__version__) < parse("2.11.0"):
            # Newer versions leave this out
            data = expand_1d(data)
        x, y, sample_weight = tf.keras.utils.unpack_x_y_sample_weight(data)
        # If the inputs are mutable dictionaries, make a shallow copy of them because we will modify
        # them during input/label pre-processing. This avoids surprising the user by wrecking their data.
        # In addition, modifying mutable Python inputs makes XLA compilation impossible.
        if isinstance(x, dict):
            x = x.copy()
        if isinstance(y, dict):
            y = y.copy()

        # When using a dummy loss, we ensure that separate labels are copied to the correct model arguments,
        # if those keys are not already present in the input dict
        if self._using_dummy_loss and y is not None:
            arg_names = list(inspect.signature(self.call).parameters)
            # If y is a tensor and the model only has one label-like input, map y to that input
            if len(label_kwargs) == 1 and isinstance(y, tf.Tensor):
                if isinstance(x, tf.Tensor):
                    x = {arg_names[0]: x}
                label_kwarg = next(iter(label_kwargs))
                if label_kwarg not in x:
                    x[label_kwarg] = y
            # Otherwise, copy keys from y to x as long as they weren't already present in x
            elif isinstance(y, dict):
                if isinstance(x, tf.Tensor):
                    x = {arg_names[0]: x}
                for key, val in y.items():
                    if key in arg_names and key not in x:
                        x[key] = val
                    elif output_to_label.get(key, None) in arg_names and key not in x:
                        x[output_to_label[key]] = val
        if y is None:
            y = {key: val for key, val in x.items() if key in label_kwargs}
            if not y and not self._using_dummy_loss:
                raise ValueError("Could not find label column(s) in input dict and no separate labels were provided!")

        if isinstance(y, dict):
            # Rename labels at this point to match output heads
            y = {label_to_output.get(key, key): val for key, val in y.items()}

        # Run forward pass.
        if self._using_dummy_loss and "return_loss" in arg_names:
            y_pred = self(x, return_loss=True, training=False)
        else:
            y_pred = self(x, training=False)
        if self._using_dummy_loss:
            loss = self.compiled_loss(y_pred.loss, y_pred.loss, sample_weight, regularization_losses=self.losses)
        else:
            loss = None

        # This next block matches outputs to label keys. Tensorflow's standard method for doing this
        # can get very confused if any of the keys contain nested values (e.g. lists/tuples of Tensors)
        if isinstance(y, dict) and len(y) == 1:
            if list(y.keys())[0] in y_pred.keys():
                y_pred = y_pred[list(y.keys())[0]]
            elif list(y_pred.keys())[0] == "loss":
                y_pred = y_pred[1]
            else:
                y_pred = y_pred[0]
            _, y = y.popitem()
        elif isinstance(y, dict):
            # If the labels are a dict, match keys from the output by name
            y_pred = {key: val for key, val in y_pred.items() if key in y}
        elif isinstance(y, tuple) or isinstance(y, list):
            # If the labels are a tuple/list, match keys to the output by order, skipping the loss.
            if list(y_pred.keys())[0] == "loss":
                y_pred = y_pred.to_tuple()[1:]
            else:
                y_pred = y_pred.to_tuple()
            y_pred = y_pred[: len(y)]  # Remove unused fields in case those cause problems
        else:
            # If the labels are a single tensor, match them to the first non-loss tensor in the output
            if list(y_pred.keys())[0] == "loss":
                y_pred = y_pred[1]
            else:
                y_pred = y_pred[0]

        if loss is None:
            loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)

        self.compiled_metrics.update_state(y, y_pred, sample_weight)
        # Collect metrics to return
        return_metrics = {}
        for metric in self.metrics:
            result = metric.result()
            if isinstance(result, dict):
                return_metrics.update(result)
            else:
                return_metrics[metric.name] = result
        return return_metrics

    def create_model_card(
        self,
        output_dir,
        model_name: str,
        language: Optional[str] = None,
        license: Optional[str] = None,
        tags: Optional[str] = None,
        finetuned_from: Optional[str] = None,
        tasks: Optional[str] = None,
        dataset_tags: Optional[Union[str, List[str]]] = None,
        dataset: Optional[Union[str, List[str]]] = None,
        dataset_args: Optional[Union[str, List[str]]] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            output_dir (`str` or `os.PathLike`):
                The folder in which to create the model card.
            model_name (`str`, *optional*):
                The name of the model.
            language (`str`, *optional*):
                The language of the model (if applicable)
            license (`str`, *optional*):
                The license of the model. Will default to the license of the pretrained model used, if the original
                model given to the `Trainer` comes from a repo on the Hub.
            tags (`str` or `List[str]`, *optional*):
                Some tags to be included in the metadata of the model card.
            finetuned_from (`str`, *optional*):
                The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo
                of the original model given to the `Trainer` (if it comes from the Hub).
            tasks (`str` or `List[str]`, *optional*):
                One or several task identifiers, to be included in the metadata of the model card.
            dataset_tags (`str` or `List[str]`, *optional*):
                One or several dataset tags, to be included in the metadata of the model card.
            dataset (`str` or `List[str]`, *optional*):
                One or several dataset identifiers, to be included in the metadata of the model card.
            dataset_args (`str` or `List[str]`, *optional*):
               One or several dataset arguments, to be included in the metadata of the model card.
        """
        # Avoids a circular import by doing this when necessary.
        from .modelcard import TrainingSummary  # tests_ignore

        training_summary = TrainingSummary.from_keras(
            self,
            keras_history=self.history,
            language=language,
            license=license,
            tags=tags,
            model_name=model_name,
            finetuned_from=finetuned_from,
            tasks=tasks,
            dataset_tags=dataset_tags,
            dataset=dataset,
            dataset_args=dataset_args,
        )
        model_card = training_summary.to_model_card()
        with open(os.path.join(output_dir, "README.md"), "w") as f:
            f.write(model_card)

    def set_input_embeddings(self, value):
        """
        Set model's input embeddings

        Args:
            value (`tf.Variable`):
                The new weights mapping hidden states to vocabulary.
        """
        main_layer = getattr(self, self.base_model_prefix)

        if main_layer is None:
            raise NotImplementedError("The model does not implements the base_model_prefix attribute.")

        try:
            main_layer.set_input_embeddings(value)
        except AttributeError:
            logger.info("Building the model")
            self.build_in_name_scope()
            main_layer.set_input_embeddings(value)

    def get_output_embeddings(self) -> Union[None, tf.keras.layers.Layer]:
        """
        Returns the model's output embeddings

        Returns:
            `tf.Variable`: The new weights mapping vocabulary to hidden states.
        """
        if self.get_lm_head() is not None:
            lm_head = self.get_lm_head()

            try:
                return lm_head.get_output_embeddings()
            except AttributeError:
                logger.info("Building the model")
                self.build_in_name_scope()

                return lm_head().get_output_embeddings()

        return None  # Overwrite for models with output embeddings

    def set_output_embeddings(self, value):
        """
        Set model's output embeddings

        Args:
            value (`tf.Variable`):
                The new weights mapping hidden states to vocabulary.
        """
        if self.get_lm_head() is not None:
            lm_head = self.get_lm_head()
            try:
                lm_head.set_output_embeddings(value)
            except AttributeError:
                logger.info("Building the model")
                self.build_in_name_scope()
                lm_head.set_output_embeddings(value)

    def get_output_layer_with_bias(self) -> Union[None, tf.keras.layers.Layer]:
        """
        Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the
        embeddings

        Return:
            `tf.keras.layers.Layer`: The layer that handles the bias, None if not an LM model.
        """
        warnings.warn(
            "The method get_output_layer_with_bias is deprecated. Please use `get_lm_head` instead.", FutureWarning
        )
        return self.get_lm_head()

    def get_prefix_bias_name(self) -> Union[None, str]:
        """
        Get the concatenated _prefix name of the bias from the model name to the parent layer

        Return:
            `str`: The _prefix name of the bias.
        """
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return None

    def get_bias(self) -> Union[None, Dict[str, tf.Variable]]:
        """
        Dict of bias attached to an LM head. The key represents the name of the bias attribute.

        Return:
            `tf.Variable`: The weights representing the bias, None if not an LM model.
        """
        if self.get_lm_head() is not None:
            lm_head = self.get_lm_head()
            try:
                return lm_head.get_bias()
            except AttributeError:
                self.build_in_name_scope()

                return lm_head.get_bias()
        return None

    def set_bias(self, value):
        """
        Set all the bias in the LM head.

        Args:
            value (`Dict[tf.Variable]`):
                All the new bias attached to an LM head.
        """
        if self.get_lm_head() is not None:
            lm_head = self.get_lm_head()
            try:
                lm_head.set_bias(value)
            except AttributeError:
                self.build_in_name_scope()
                lm_head.set_bias(value)

    def get_lm_head(self) -> tf.keras.layers.Layer:
        """
        The LM Head layer. This method must be overwritten by all the models that have a lm head.

        Return:
            `tf.keras.layers.Layer`: The LM head layer if the model has one, None if not.
        """
        return None

    def resize_token_embeddings(
        self, new_num_tokens: Optional[int] = None
    ) -> Union[tf.keras.layers.Embedding, tf.Variable]:
        """
        Resizes input token embeddings matrix of the model if `new_num_tokens != config.vocab_size`.

        Takes care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.

        Arguments:
            new_num_tokens (`int`, *optional*):
                The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
                vectors at the end. Reducing the size will remove vectors from the end. If not provided or `None`, just
                returns a pointer to the input tokens without doing anything.

        Return:
            `tf.Variable` or `tf.keras.layers.Embedding`: Pointer to the input tokens of the model.
        """
        # TODO (joao): flagged for replacement (by `_v2_resized_token_embeddings`) due to embeddings refactor

        # Run the new code path if the model has a keras embeddings layer
        if isinstance(self.get_input_embeddings(), tf.keras.layers.Embedding):
            return self._v2_resized_token_embeddings(new_num_tokens)

        if new_num_tokens is None or new_num_tokens == self.config.vocab_size:
            return self._get_word_embedding_weight(self.get_input_embeddings())

        model_embeds = self._resize_token_embeddings(new_num_tokens)

        # Update base model and current model config
        self.config.vocab_size = new_num_tokens

        return model_embeds

    def _v2_resized_token_embeddings(self, new_num_tokens: Optional[int] = None) -> tf.keras.layers.Embedding:
        """
        Resizes input token embeddings matrix of the model if `new_num_tokens != config.vocab_size`.

        Arguments:
            new_num_tokens (`int`, *optional*):
                The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
                vectors at the end. Reducing the size will remove vectors from the end. If not provided or `None`, just
                returns a pointer to the input tokens without doing anything.

        Return:
            `tf.keras.layers.Embedding`: Pointer to the input tokens of the model.
        """
        if new_num_tokens is None or new_num_tokens == self.config.vocab_size:
            return self.get_input_embeddings()

        model_embeds = self._v2_resize_token_embeddings(new_num_tokens)

        # Update base model and current model config
        self.config.vocab_size = new_num_tokens

        return model_embeds

    def _get_word_embedding_weight(model, embedding_layer):
        # TODO (joao): flagged for delection due to embeddings refactor

        # If the variable holds the weights themselves, return them
        if isinstance(embedding_layer, tf.Tensor):
            return embedding_layer
        # Otherwise, try to get them from the layer's attributes

        embeds = getattr(embedding_layer, "weight", None)
        if embeds is not None:
            return embeds

        embeds = getattr(embedding_layer, "decoder", None)
        if embeds is not None:
            return embeds

        # The reason why the attributes don't exist might be
        # because the model is not built, so retry getting
        # the argument after building the model
        model.build_in_name_scope()

        embeds = getattr(embedding_layer, "weight", None)
        if embeds is not None:
            return embeds

        embeds = getattr(embedding_layer, "decoder", None)
        if embeds is not None:
            return embeds

        return None

    def _resize_token_embeddings(self, new_num_tokens):
        # TODO (joao): flagged for replacement (by `_v2_resize_token_embeddings`) due to embeddings refactor
        old_embeddings = self._get_word_embedding_weight(self.get_input_embeddings())
        new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)

        # if word embeddings are not tied, make sure that lm head bias is resized as well
        if self.get_bias() is not None:
            old_lm_head_bias = self.get_bias()
            new_lm_head_bias = self._get_resized_lm_head_bias(old_lm_head_bias, new_num_tokens)

            self.set_bias(new_lm_head_bias)

        # if word embeddings are not tied, make sure that lm head decoder is resized as well
        if self.get_output_embeddings() is not None:
            old_lm_head_decoder = self._get_word_embedding_weight(self.get_output_embeddings())
            new_lm_head_decoder = self._get_resized_lm_head_decoder(old_lm_head_decoder, new_num_tokens)

            self.set_output_embeddings(new_lm_head_decoder)

        self.set_input_embeddings(new_embeddings)

        return self.get_input_embeddings()

    def _v2_resize_token_embeddings(self, new_num_tokens):
        old_embeddings = self.get_input_embeddings()
        new_embeddings = self._v2_get_resized_embeddings(old_embeddings, new_num_tokens)
        self.set_input_embeddings(new_embeddings)

        # If word embeddings are not tied, make sure that lm head bias is resized as well
        if self.get_bias() is not None:
            old_lm_head_bias = self.get_bias()
            new_lm_head_bias = self._v2_get_resized_lm_head_bias(old_lm_head_bias, new_num_tokens)
            self.set_bias(new_lm_head_bias)

        # If word embeddings are not tied, make sure that lm head decoder is resized as well.
        tied_weights = self.get_input_embeddings() == self.get_output_embeddings()
        if self.get_output_embeddings() is not None and not tied_weights:
            old_lm_head_decoder = self._get_word_embedding_weight(self.get_output_embeddings())
            # TODO (joao): this one probably needs a v2 version with other models
            new_lm_head_decoder = self._get_resized_lm_head_decoder(old_lm_head_decoder, new_num_tokens)
            self.set_output_embeddings(new_lm_head_decoder)

        return self.get_input_embeddings()

    def _get_resized_lm_head_bias(self, old_lm_head_bias, new_num_tokens):
        """
        Build a resized bias from the old ones. Increasing the size will add newly initialized vectors at the end.
        Reducing the size will remove vectors from the end

        Args:
            old_lm_head_bias (`tf.Variable`):
                Old lm head bias to be resized.
            new_num_tokens (`int`, *optional*):
                New number of tokens in the linear matrix.

                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
                vectors from the end. If not provided or `None`, just returns None

        Return:
            `tf.Variable`: Pointer to the resized bias.
        """
        # TODO (joao): flagged for replacement (by `_v2_get_resized_lm_head_bias`) due to embeddings refactor
        new_lm_head_bias = {}

        for attr, weight in old_lm_head_bias.items():
            first_dim, old_num_tokens = (None, shape_list(weight)[0]) if tf.rank(weight) == 1 else shape_list(weight)
            size_diff = new_num_tokens - old_num_tokens
            final_shape = [new_num_tokens] if first_dim is None else [first_dim, new_num_tokens]

            # initialize new bias
            if tf.math.greater(size_diff, 0):
                padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]]
                current_bias = tf.pad(weight.value(), tf.convert_to_tensor(padding_shape), constant_values=-1)
                num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
                mask_shape = [num_tokens_to_copy] if first_dim is None else [1, num_tokens_to_copy]
                bias_mask = tf.fill(tf.convert_to_tensor(mask_shape), True)
                bias_mask = tf.pad(bias_mask, tf.convert_to_tensor(padding_shape), constant_values=False)
            else:
                slice_from = [0] if first_dim is None else [0, 0]
                current_bias = tf.slice(
                    weight.value(), tf.convert_to_tensor(slice_from), tf.convert_to_tensor(final_shape)
                )
                bias_mask = tf.fill(tf.convert_to_tensor(final_shape), True)

            new_bias = self.add_weight(
                shape=final_shape,
                initializer="zeros",
                trainable=True,
                name=weight.name.split(":")[0],
            )
            init_bias = tf.where(bias_mask, current_bias, new_bias.value())

            new_bias.assign(init_bias)
            new_lm_head_bias[attr] = new_bias

        return new_lm_head_bias

    def _v2_get_resized_lm_head_bias(
        self, old_lm_head_bias: Dict[str, tf.Variable], new_num_tokens: int
    ) -> Dict[str, tf.Tensor]:
        """
        Build a resized bias from the old ones. Increasing the size will add newly initialized vectors at the end.
        Reducing the size will remove vectors from the end

        Args:
            old_lm_head_bias (`Dict[str, tf.Variable]`):
                Old lm head bias to be resized.
            new_num_tokens (`int`):
                New number of tokens in the linear matrix. Increasing the size will add newly initialized vectors at
                the end. Reducing the size will remove vectors from the end.

        Return:
            `tf.Tensor`: Values for the resized bias.
        """
        new_lm_head_bias = {}

        for attr, weight in old_lm_head_bias.items():
            # Determine the size difference (depending on the shape)
            first_dim, old_num_tokens = (None, shape_list(weight)[0]) if tf.rank(weight) == 1 else shape_list(weight)
            size_diff = new_num_tokens - old_num_tokens

            # Copy the old bias values to the new bias
            if old_num_tokens > new_num_tokens:
                new_bias = weight.value()[..., :new_num_tokens]
            else:
                padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]]
                new_bias = tf.pad(weight.value(), tf.convert_to_tensor(padding_shape))

            new_lm_head_bias[attr] = new_bias
        return new_lm_head_bias

    def _get_resized_lm_head_decoder(self, old_lm_head_decoder, new_num_tokens):
        """
        Build a resized decoder from the old ones. Increasing the size will add newly initialized vectors at the end.
        Reducing the size will remove vectors from the end

        Args:
            old_lm_head_decoder (`tf.Variable`):
                Old lm head decoder to be resized.
            new_num_tokens (`int`, *optional*):
                New number of tokens in the linear matrix.

                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
                vectors from the end. If not provided or `None`, just returns None

        Return:
            `tf.Variable`: Pointer to the resized decoder or None if the output embeddings are different from the input
            ones.
        """
        new_lm_head_decoder = old_lm_head_decoder
        is_input_output_equals = tf.reduce_any(
            self._get_word_embedding_weight(self.get_input_embeddings()) == old_lm_head_decoder
        )

        if old_lm_head_decoder is not None and not is_input_output_equals:
            old_embedding_dim = shape_list(old_lm_head_decoder)[1]
            decoder_mask, current_decoder = init_copy_embeddings(old_lm_head_decoder, new_num_tokens)
            new_lm_head_decoder = self.add_weight(
                shape=(new_num_tokens, old_embedding_dim),
                initializer="zeros",
                trainable=True,
                name=old_lm_head_decoder.name.split(":")[0],
            )
            init_decoder = tf.where(decoder_mask, current_decoder, new_lm_head_decoder.value())

            new_lm_head_decoder.assign(init_decoder)

        return new_lm_head_decoder

    def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None) -> tf.Variable:
        """
        Build a resized Embedding weights from a provided token Embedding weights. Increasing the size will add newly
        initialized vectors at the end. Reducing the size will remove vectors from the end

        Args:
            old_embeddings (`tf.Variable`):
                Old embeddings to be resized.
            new_num_tokens (`int`, *optional*):
                New number of tokens in the embedding matrix.

                Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
                vectors from the end. If not provided or `None`, just returns a pointer to the input tokens
                `tf.Variable` module of the model without doing anything.

        Return:
            `tf.Variable`: Pointer to the resized Embedding Module or the old Embedding Module if `new_num_tokens` is
            `None`
        """
        # TODO (joao): flagged for replacement (by `_v2_get_resized_embeddings`) due to embeddings refactor
        old_embedding_dim = shape_list(old_embeddings)[1]
        init_range = getattr(self.config, "initializer_range", 0.02)
        embeddings_mask, current_embeddings = init_copy_embeddings(old_embeddings, new_num_tokens)
        new_embeddings = self.add_weight(
            name=old_embeddings.name.split(":")[0],
            shape=[new_num_tokens, old_embedding_dim],
            initializer=get_initializer(init_range),
            dtype=tf.float32,
        )
        init_embeddings = tf.where(embeddings_mask, current_embeddings, new_embeddings.value())

        new_embeddings.assign(init_embeddings)

        return new_embeddings

    def _v2_get_resized_embeddings(
        self, old_embeddings: tf.keras.layers.Embedding, new_num_tokens: int
    ) -> tf.keras.layers.Embedding:
        """
        Build a resized Embedding layer from a provided Embedding layer. Increasing the size will add newly initialized
        vectors at the end. Reducing the size will remove vectors from the end.

        Args:
            old_embeddings (`tf.keras.layers.Embedding`):
                Old embeddings to be resized.
            new_num_tokens (`int`, *optional*):
                New number of tokens in the embedding matrix.

        Return:
            `tf.keras.layers.Embedding`: Resized Embedding layer.
        """

        # Get the initialization range for the embeddings
        init_range = 0.02  # default value
        potential_initialization_variable_names = [
            "initializer_range",  # most common
            "initializer_factor",  # e.g. T5
            "init_std",  # e.g BART
        ]
        for var_name in potential_initialization_variable_names:
            if hasattr(self.config, var_name):
                init_range = getattr(self.config, var_name)

        # Get a new (initialized) embeddings layer
        new_embeddings = tf.keras.layers.Embedding(
            input_dim=new_num_tokens,
            output_dim=old_embeddings.output_dim,
            embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=init_range),
            name=old_embeddings.embeddings.name[:-13],  # exact same scoped name except "/embeddings:0"
        )
        new_embeddings(tf.constant([[0]]))

        # Copy the old embeddings to the new embeddings
        if old_embeddings.input_dim >= new_num_tokens:
            init_embeddings = old_embeddings.embeddings[:new_num_tokens]
        else:
            init_embeddings = tf.concat(
                [old_embeddings.embeddings, new_embeddings.embeddings[old_embeddings.input_dim :]], axis=0
            )
        new_embeddings.embeddings.assign(init_embeddings)
        return new_embeddings

    def prune_heads(self, heads_to_prune):
        """
        Prunes heads of the base model.

        Arguments:
            heads_to_prune (`Dict[int, List[int]]`):
                Dictionary with keys being selected layer indices (`int`) and associated values being the list of heads
                to prune in said layer (list of `int`). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on
                layer 1 and heads 2 and 3 on layer 2.
        """
        raise NotImplementedError

    def save_pretrained(
        self,
        save_directory,
        saved_model=False,
        version=1,
        push_to_hub=False,
        signatures=None,
        max_shard_size: Union[int, str] = "10GB",
        create_pr: bool = False,
        safe_serialization: bool = False,
        token: Optional[Union[str, bool]] = None,
        **kwargs,
    ):
        """
        Save a model and its configuration file to a directory, so that it can be re-loaded using the
        [`~TFPreTrainedModel.from_pretrained`] class method.

        Arguments:
            save_directory (`str`):
                Directory to which to save. Will be created if it doesn't exist.
            saved_model (`bool`, *optional*, defaults to `False`):
                If the model has to be saved in saved model format as well or not.
            version (`int`, *optional*, defaults to 1):
                The version of the saved model. A saved model needs to be versioned in order to be properly loaded by
                TensorFlow Serving as detailed in the official documentation
                https://www.tensorflow.org/tfx/serving/serving_basic
            push_to_hub (`bool`, *optional*, defaults to `False`):
                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                namespace).
            signatures (`dict` or `tf.function`, *optional*):
                Model's signature used for serving. This will be passed to the `signatures` argument of model.save().
            max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`):
                The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
                lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`).

                <Tip warning={true}>

                If a single weight of the model is bigger than `max_shard_size`, it will be in its own checkpoint shard
                which will be bigger than `max_shard_size`.

                </Tip>

            create_pr (`bool`, *optional*, defaults to `False`):
                Whether or not to create a PR with the uploaded files or directly commit.
            safe_serialization (`bool`, *optional*, defaults to `False`):
                Whether to save the model using `safetensors` or the traditional TensorFlow way (that uses `h5`).
            token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
            kwargs (`Dict[str, Any]`, *optional*):
                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        use_auth_token = kwargs.pop("use_auth_token", None)

        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
                FutureWarning,
            )
            if token is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            token = use_auth_token

        if token is not None:
            kwargs["token"] = token

        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        os.makedirs(save_directory, exist_ok=True)

        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = self._create_repo(repo_id, **kwargs)
            files_timestamps = self._get_files_timestamps(save_directory)

        if saved_model:
            # If `torch_dtype` is in the config with a torch dtype class as the value, we need to change it to string.
            # (Although TF doesn't care about this attribute, we can't just remove it or set it to `None`.)
            if getattr(self.config, "torch_dtype", None) is not None and not isinstance(self.config.torch_dtype, str):
                self.config.torch_dtype = str(self.config.torch_dtype).split(".")[1]
            if signatures is None:
                serving_default = self.serving.get_concrete_function(self.input_signature)
                if any(spec.dtype == tf.int32 for spec in self.input_signature.values()):
                    int64_spec = {
                        key: tf.TensorSpec(
                            shape=spec.shape, dtype=tf.int64 if spec.dtype == tf.int32 else spec.dtype, name=spec.name
                        )
                        for key, spec in self.input_signature.items()
                    }
                    int64_serving = self.serving.get_concrete_function(int64_spec)
                    signatures = {"serving_default": serving_default, "int64_serving": int64_serving}
                else:
                    signatures = serving_default
            saved_model_dir = os.path.join(save_directory, "saved_model", str(version))
            self.save(saved_model_dir, include_optimizer=False, signatures=signatures)
            logger.info(f"Saved model created in {saved_model_dir}")

        # Save configuration file
        self.config.architectures = [self.__class__.__name__[2:]]

        # If we have a custom model, we copy the file defining it in the folder and set the attributes so it can be
        # loaded from the Hub.
        if self._auto_class is not None:
            custom_object_save(self, save_directory, config=self.config)

        self.config.save_pretrained(save_directory)
        if self.can_generate():
            self.generation_config.save_pretrained(save_directory)

        # If we save using the predefined names, we can load using `from_pretrained`
        weights_name = SAFE_WEIGHTS_NAME if safe_serialization else TF2_WEIGHTS_NAME
        output_model_file = os.path.join(save_directory, weights_name)

        shards, index = tf_shard_checkpoint(self.weights, max_shard_size)

        # Clean the folder from a previous save
        for filename in os.listdir(save_directory):
            full_filename = os.path.join(save_directory, filename)
            # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
            # in distributed settings to avoid race conditions.
            weights_no_suffix = weights_name.replace(".bin", "").replace(".safetensors", "")
            if (
                filename.startswith(weights_no_suffix)
                and os.path.isfile(full_filename)
                and filename not in shards.keys()
            ):
                os.remove(full_filename)

        if index is None:
            if safe_serialization:
                state_dict = {strip_model_name_and_prefix(w.name): w.value() for w in self.weights}
                safe_save_file(state_dict, output_model_file, metadata={"format": "tf"})
            else:
                self.save_weights(output_model_file)
            logger.info(f"Model weights saved in {output_model_file}")
        else:
            save_index_file = os.path.join(save_directory, TF2_WEIGHTS_INDEX_NAME)
            # Save the index as well
            with open(save_index_file, "w", encoding="utf-8") as index_file:
                content = json.dumps(index, indent=2, sort_keys=True) + "\n"
                index_file.write(content)
            logger.info(
                f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
                f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
                f"index located at {save_index_file}."
            )
            for shard_file, shard in shards.items():
                with h5py.File(os.path.join(save_directory, shard_file), mode="w") as shard_file:
                    layers = []
                    for layer in sorted(shard, key=lambda x: x.name):
                        if "model." in layer.name or len(layer.name.split("/")) == 1:
                            layer_name = layer.name
                        else:
                            layer_name = "/".join(layer.name.split("/")[1:])
                        param_dset = shard_file.create_dataset(
                            layer_name, layer.numpy().shape, dtype=layer.numpy().dtype
                        )
                        param_dset[:] = layer.numpy()
                        layers.append(layer_name.encode("utf8"))
                    save_attributes_to_hdf5_group(shard_file, "layer_names", layers)

        if push_to_hub:
            self._upload_modified_files(
                save_directory,
                repo_id,
                files_timestamps,
                commit_message=commit_message,
                token=token,
            )

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
        *model_args,
        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        ignore_mismatched_sizes: bool = False,
        force_download: bool = False,
        local_files_only: bool = False,
        token: Optional[Union[str, bool]] = None,
        revision: str = "main",
        **kwargs,
    ):
        r"""
        Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.

        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
        task.

        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
        weights are discarded.

        Parameters:
            pretrained_model_name_or_path (`str`, *optional*):
                Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                      user or organization name, like `dbmdz/bert-base-german-cased`.
                    - A path to a *directory* containing model weights saved using
                      [`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
                    - A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
                      case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
                      argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
                      using the provided conversion scripts and loading the TensorFlow model afterwards.
                    - `None` if you are both providing the configuration and state dictionary (resp. with keyword
                      arguments `config` and `state_dict`).
            model_args (sequence of positional arguments, *optional*):
                All remaining positional arguments will be passed to the underlying model's `__init__` method.
            config (`Union[PretrainedConfig, str]`, *optional*):
                Can be either:

                    - an instance of a class derived from [`PretrainedConfig`],
                    - a string valid as input to [`~PretrainedConfig.from_pretrained`].

                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
                be automatically loaded when:

                    - The model is a model provided by the library (loaded with the *model id* string of a pretrained
                      model).
                    - The model was saved using [`~TFPreTrainedModel.save_pretrained`] and is reloaded by supplying the
                      save directory.
                    - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
                      configuration JSON file named *config.json* is found in the directory.
            from_pt (`bool`, *optional*, defaults to `False`):
                Load the model weights from a PyTorch state_dict save file (see docstring of
                `pretrained_model_name_or_path` argument).
            ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
                Whether or not to raise an error if some of the weights from the checkpoint do not have the same size
                as the weights of the model (if for instance, you are instantiating a model with 10 labels from a
                checkpoint with 3 labels).
            cache_dir (`str`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies:
                (`Dict[str, str], `optional`): A dictionary of proxy servers to use by protocol or endpoint, e.g.,
                `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
                output_loading_info(`bool`, *optional*, defaults to `False`): Whether ot not to also return a
                dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (e.g., not try downloading the model).
            token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.


                <Tip>

                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".

                </Tip>

            mirror (`str`, *optional*):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                Please refer to the mirror site for more information.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            tf_to_pt_weight_rename (`Callable`, *optional*):
                A function that is called to transform the names of weights during the PyTorch to TensorFlow
                crossloading process. This is not necessary for most models, but is useful to allow composite models to
                be crossloaded correctly.
            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
                automatically loaded:

                    - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
                      underlying model's `__init__` method (we assume all relevant updates to the configuration have
                      already been done)
                    - If a configuration is not provided, `kwargs` will be first passed to the configuration class
                      initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
                      corresponds to a configuration attribute will be used to override said attribute with the
                      supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
                      will be passed to the underlying model's `__init__` function.

        Examples:

        ```python
        >>> from transformers import BertConfig, TFBertModel

        >>> # Download model and configuration from huggingface.co and cache.
        >>> model = TFBertModel.from_pretrained("bert-base-uncased")
        >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
        >>> model = TFBertModel.from_pretrained("./test/saved_model/")
        >>> # Update configuration during loading.
        >>> model = TFBertModel.from_pretrained("bert-base-uncased", output_attentions=True)
        >>> assert model.config.output_attentions == True
        >>> # Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).
        >>> config = BertConfig.from_json_file("./pt_model/my_pt_model_config.json")
        >>> model = TFBertModel.from_pretrained("./pt_model/my_pytorch_model.bin", from_pt=True, config=config)
        ```"""
        from_pt = kwargs.pop("from_pt", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        output_loading_info = kwargs.pop("output_loading_info", False)
        use_auth_token = kwargs.pop("use_auth_token", None)
        trust_remote_code = kwargs.pop("trust_remote_code", None)
        _ = kwargs.pop("mirror", None)
        load_weight_prefix = kwargs.pop("load_weight_prefix", None)
        from_pipeline = kwargs.pop("_from_pipeline", None)
        from_auto_class = kwargs.pop("_from_auto", False)
        subfolder = kwargs.pop("subfolder", "")
        commit_hash = kwargs.pop("_commit_hash", None)
        tf_to_pt_weight_rename = kwargs.pop("tf_to_pt_weight_rename", None)

        # Not relevant for TF models
        _ = kwargs.pop("adapter_kwargs", None)

        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
                FutureWarning,
            )
            if token is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            token = use_auth_token

        if trust_remote_code is True:
            logger.warning(
                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
                " ignored."
            )

        user_agent = {"file_type": "model", "framework": "tensorflow", "from_auto_class": from_auto_class}
        if from_pipeline is not None:
            user_agent["using_pipeline"] = from_pipeline

        if is_offline_mode() and not local_files_only:
            logger.info("Offline mode: forcing local_files_only=True")
            local_files_only = True

        # Load config if we don't provide a configuration
        if not isinstance(config, PretrainedConfig):
            config_path = config if config is not None else pretrained_model_name_or_path
            config, model_kwargs = cls.config_class.from_pretrained(
                config_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                _from_auto=from_auto_class,
                _from_pipeline=from_pipeline,
                _commit_hash=commit_hash,
                **kwargs,
            )
        else:
            model_kwargs = kwargs

        if commit_hash is None:
            commit_hash = getattr(config, "_commit_hash", None)

        # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
        # index of the files.
        is_sharded = False
        # Load model
        if pretrained_model_name_or_path is not None:
            pretrained_model_name_or_path = str(pretrained_model_name_or_path)
            is_local = os.path.isdir(pretrained_model_name_or_path)
            if is_local:
                if from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
                    # Load from a PyTorch checkpoint in priority if from_pt
                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
                elif from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)):
                    # Load from a sharded PyTorch checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)
                    is_sharded = True
                elif is_safetensors_available() and os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
                ):
                    # Load from a safetensors checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)):
                    # Load from a TF 2.0 checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_NAME)
                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME)):
                    # Load from a sharded TF 2.0 checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME)
                    is_sharded = True
                elif is_safetensors_available() and os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME)
                ):
                    # Load from a sharded safetensors checkpoint
                    archive_file = os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME)
                    is_sharded = True
                    raise NotImplementedError("Support for sharded checkpoints using safetensors is coming soon!")
                # At this stage we don't have a weight file so we will raise an error.
                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)) or os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)
                ):
                    raise EnvironmentError(
                        f"Error no file named {TF2_WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} "
                        "but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those "
                        "weights."
                    )
                else:
                    raise EnvironmentError(
                        f"Error no file named {TF2_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
                        f"{pretrained_model_name_or_path}."
                    )
            elif os.path.isfile(pretrained_model_name_or_path):
                archive_file = pretrained_model_name_or_path
                is_local = True
            elif os.path.isfile(pretrained_model_name_or_path + ".index"):
                archive_file = pretrained_model_name_or_path + ".index"
                is_local = True
            elif is_remote_url(pretrained_model_name_or_path):
                filename = pretrained_model_name_or_path
                resolved_archive_file = download_url(pretrained_model_name_or_path)
            else:
                # set correct filename
                if from_pt:
                    filename = WEIGHTS_NAME
                elif is_safetensors_available():
                    filename = SAFE_WEIGHTS_NAME
                else:
                    filename = TF2_WEIGHTS_NAME

                try:
                    # Load from URL or cache if already cached
                    cached_file_kwargs = {
                        "cache_dir": cache_dir,
                        "force_download": force_download,
                        "proxies": proxies,
                        "resume_download": resume_download,
                        "local_files_only": local_files_only,
                        "token": token,
                        "user_agent": user_agent,
                        "revision": revision,
                        "subfolder": subfolder,
                        "_raise_exceptions_for_missing_entries": False,
                        "_commit_hash": commit_hash,
                    }
                    resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs)

                    # Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None
                    # result when internet is up, the repo and revision exist, but the file does not.
                    if resolved_archive_file is None and filename == SAFE_WEIGHTS_NAME:
                        # Did not find the safetensors file, let's fallback to TF.
                        # No support for sharded safetensors yet, so we'll raise an error if that's all we find.
                        filename = TF2_WEIGHTS_NAME
                        resolved_archive_file = cached_file(
                            pretrained_model_name_or_path, TF2_WEIGHTS_NAME, **cached_file_kwargs
                        )
                    if resolved_archive_file is None and filename == TF2_WEIGHTS_NAME:
                        # Maybe the checkpoint is sharded, we try to grab the index name in this case.
                        resolved_archive_file = cached_file(
                            pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME, **cached_file_kwargs
                        )
                        if resolved_archive_file is not None:
                            is_sharded = True
                    if resolved_archive_file is None and filename == WEIGHTS_NAME:
                        # Maybe the checkpoint is sharded, we try to grab the index name in this case.
                        resolved_archive_file = cached_file(
                            pretrained_model_name_or_path, WEIGHTS_INDEX_NAME, **cached_file_kwargs
                        )
                        if resolved_archive_file is not None:
                            is_sharded = True
                    if resolved_archive_file is None:
                        # Otherwise, maybe there is a PyTorch or Flax model file.  We try those to give a helpful error
                        # message.
                        has_file_kwargs = {
                            "revision": revision,
                            "proxies": proxies,
                            "token": token,
                        }
                        if has_file(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME, **has_file_kwargs):
                            is_sharded = True
                            raise NotImplementedError(
                                "Support for sharded checkpoints using safetensors is coming soon!"
                            )
                        elif has_file(pretrained_model_name_or_path, WEIGHTS_NAME, **has_file_kwargs):
                            raise EnvironmentError(
                                f"{pretrained_model_name_or_path} does not appear to have a file named"
                                f" {TF2_WEIGHTS_NAME} but there is a file for PyTorch weights. Use `from_pt=True` to"
                                " load this model from those weights."
                            )
                        else:
                            raise EnvironmentError(
                                f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME},"
                                f" {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
                            )

                except EnvironmentError:
                    # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                    # to the original exception.
                    raise
                except Exception:
                    # For any other exception, we throw a generic error.

                    raise EnvironmentError(
                        f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
                        " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                        f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
                        f" directory containing a file named {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
                    )
            if is_local:
                logger.info(f"loading weights file {archive_file}")
                resolved_archive_file = archive_file
                filename = resolved_archive_file.split(os.path.sep)[-1]
            else:
                logger.info(f"loading weights file {filename} from cache at {resolved_archive_file}")
        else:
            resolved_archive_file = None

        # We'll need to download and cache each checkpoint shard if the checkpoint is sharded.
        if is_sharded:
            # resolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.
            resolved_archive_file, _ = get_checkpoint_shard_files(
                pretrained_model_name_or_path,
                resolved_archive_file,
                cache_dir=cache_dir,
                force_download=force_download,
                proxies=proxies,
                resume_download=resume_download,
                local_files_only=local_files_only,
                token=token,
                user_agent=user_agent,
                revision=revision,
                _commit_hash=commit_hash,
            )

        safetensors_from_pt = False
        if filename == SAFE_WEIGHTS_NAME:
            with safe_open(resolved_archive_file, framework="tf") as f:
                safetensors_metadata = f.metadata()
            if safetensors_metadata is None or safetensors_metadata.get("format") not in ["pt", "tf", "flax"]:
                raise OSError(
                    f"The safetensors archive passed at {resolved_archive_file} does not contain the valid metadata."
                    " Make sure you save your model with the `save_pretrained` method."
                )
            safetensors_from_pt = safetensors_metadata.get("format") == "pt"

        config.name_or_path = pretrained_model_name_or_path

        # composed models, *e.g.* TFRag, require special treatment when it comes to loading
        # pre-trained weights.
        if cls._requires_load_weight_prefix and model_kwargs.get("name") is not None:
            model_kwargs["load_weight_prefix"] = load_weight_prefix + "/" + model_kwargs.get("name")

        # Instantiate model.
        model = cls(config, *model_args, **model_kwargs)

        if tf_to_pt_weight_rename is None and hasattr(model, "tf_to_pt_weight_rename"):
            # TODO Matt: This is a temporary workaround to allow weight renaming, but requires a method
            #            to be defined for each class that requires a rename. We can probably just have a class-level
            #            dict and a single top-level method or something and cut down a lot of boilerplate code
            tf_to_pt_weight_rename = model.tf_to_pt_weight_rename

        if from_pt:
            from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model

            # Load from a PyTorch checkpoint
            return load_pytorch_checkpoint_in_tf2_model(
                model,
                resolved_archive_file,
                allow_missing_keys=True,
                output_loading_info=output_loading_info,
                _prefix=load_weight_prefix,
                tf_to_pt_weight_rename=tf_to_pt_weight_rename,
            )

        # we might need to extend the variable scope for composite models
        if load_weight_prefix is not None:
            with tf.compat.v1.variable_scope(load_weight_prefix):
                model.build_in_name_scope()  # build the network with dummy inputs
        else:
            model.build_in_name_scope()  # build the network with dummy inputs

        if safetensors_from_pt:
            from .modeling_tf_pytorch_utils import load_pytorch_state_dict_in_tf2_model

            with safe_open(resolved_archive_file, framework="tf") as safetensors_archive:
                # Load from a PyTorch checkpoint
                # We load in TF format here because PT weights often need to be transposed, and this is much
                # faster on GPU. Loading as numpy and transposing on CPU adds several seconds to load times.
                return load_pytorch_state_dict_in_tf2_model(
                    model,
                    safetensors_archive,
                    tf_inputs=False,  # No need to build the model again
                    allow_missing_keys=True,
                    output_loading_info=output_loading_info,
                    _prefix=load_weight_prefix,
                    ignore_mismatched_sizes=ignore_mismatched_sizes,
                    tf_to_pt_weight_rename=tf_to_pt_weight_rename,
                )

        # 'by_name' allow us to do transfer learning by skipping/adding layers
        # see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
        try:
            if is_sharded:
                for file in resolved_archive_file:
                    os.path.isfile(file), f"Error retrieving files {file}"

                missing_keys, unexpected_keys, mismatched_keys = load_tf_sharded_weights(
                    model,
                    resolved_archive_file,
                    ignore_mismatched_sizes=ignore_mismatched_sizes,
                    _prefix=load_weight_prefix,
                )
            else:
                missing_keys, unexpected_keys, mismatched_keys = load_tf_weights(
                    model,
                    resolved_archive_file,
                    ignore_mismatched_sizes=ignore_mismatched_sizes,
                    _prefix=load_weight_prefix,
                )
        except OSError as e:
            try:
                with open(resolved_archive_file) as f:
                    if f.read().startswith("version"):
                        raise OSError(
                            "You seem to have cloned a repository without having git-lfs installed. Please install "
                            "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder "
                            "you cloned."
                        )
                    else:
                        raise ValueError from e
            except (UnicodeDecodeError, ValueError):
                raise OSError(
                    "Unable to load weights from h5 file. "
                    "If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True. "
                )

        if cls._keys_to_ignore_on_load_missing is not None:
            for pat in cls._keys_to_ignore_on_load_missing:
                missing_keys = [k for k in missing_keys if re.search(pat, k) is None]

        if cls._keys_to_ignore_on_load_unexpected is not None:
            for pat in cls._keys_to_ignore_on_load_unexpected:
                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

        if len(unexpected_keys) > 0:
            logger.warning(
                f"Some layers from the model checkpoint at {pretrained_model_name_or_path} were not used when"
                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"
                " with another architecture (e.g. initializing a BertForSequenceClassification model from a"
                " BertForPreTraining model).\n- This IS NOT expected if you are initializing"
                f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly identical"
                " (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)."
            )
        else:
            logger.warning(f"All model checkpoint layers were used when initializing {model.__class__.__name__}.\n")

        if len(missing_keys) > 0:
            logger.warning(
                f"Some layers of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
            )
        elif len(mismatched_keys) == 0:
            logger.warning(
                f"All the layers of {model.__class__.__name__} were initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint"
                f" was trained on, you can already use {model.__class__.__name__} for predictions without further"
                " training."
            )
        if len(mismatched_keys) > 0:
            mismatched_warning = "\n".join(
                [
                    f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
                    for key, shape1, shape2 in mismatched_keys
                ]
            )
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
                f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be able"
                " to use it for predictions and inference."
            )

        # If it is a model with generation capabilities, attempt to load the generation config
        if model.can_generate():
            try:
                model.generation_config = GenerationConfig.from_pretrained(
                    pretrained_model_name_or_path,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    revision=revision,
                    subfolder=subfolder,
                    _from_auto=from_auto_class,
                    _from_pipeline=from_pipeline,
                    **kwargs,
                )
            except OSError:
                logger.info(
                    "Generation config file not found, using a generation config created from the model config."
                )
                pass

        if output_loading_info:
            loading_info = {
                "missing_keys": missing_keys,
                "unexpected_keys": unexpected_keys,
                "mismatched_keys": mismatched_keys,
            }

            return model, loading_info

        return model

    def push_to_hub(
        self,
        repo_id: str,
        use_temp_dir: Optional[bool] = None,
        commit_message: Optional[str] = None,
        private: Optional[bool] = None,
        max_shard_size: Optional[Union[int, str]] = "10GB",
        token: Optional[Union[bool, str]] = None,
        # (`use_auth_token` is deprecated: we have to keep it here as we don't have **kwargs)
        use_auth_token: Optional[Union[bool, str]] = None,
        create_pr: bool = False,
        **base_model_card_args,
    ) -> str:
        """
        Upload the model files to the 🤗 Model Hub while synchronizing a local clone of the repo in `repo_path_or_name`.

        Parameters:
            repo_id (`str`):
                The name of the repository you want to push your model to. It should contain your organization name
                when pushing to a given organization.
            use_temp_dir (`bool`, *optional*):
                Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub.
                Will default to `True` if there is no directory named like `repo_id`, `False` otherwise.
            commit_message (`str`, *optional*):
                Message to commit while pushing. Will default to `"Upload model"`.
            private (`bool`, *optional*):
                Whether or not the repository created should be private.
            token (`bool` or `str`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`
                is not specified.
            max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`):
                Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard
                will then be each of size lower than this size. If expressed as a string, needs to be digits followed
                by a unit (like `"5MB"`).
            create_pr (`bool`, *optional*, defaults to `False`):
                Whether or not to create a PR with the uploaded files or directly commit.

        Examples:

        ```python
        from transformers import TFAutoModel

        model = TFAutoModel.from_pretrained("bert-base-cased")

        # Push the model to your namespace with the name "my-finetuned-bert".
        model.push_to_hub("my-finetuned-bert")

        # Push the model to an organization with the name "my-finetuned-bert".
        model.push_to_hub("huggingface/my-finetuned-bert")
        ```
        """
        if use_auth_token is not None:
            warnings.warn(
                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
                FutureWarning,
            )
            if token is not None:
                raise ValueError(
                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
                )
            token = use_auth_token

        if "repo_path_or_name" in base_model_card_args:
            warnings.warn(
                "The `repo_path_or_name` argument is deprecated and will be removed in v5 of Transformers. Use "
                "`repo_id` instead."
            )
            repo_id = base_model_card_args.pop("repo_path_or_name")
        # Deprecation warning will be sent after for repo_url and organization
        repo_url = base_model_card_args.pop("repo_url", None)
        organization = base_model_card_args.pop("organization", None)

        if os.path.isdir(repo_id):
            working_dir = repo_id
            repo_id = repo_id.split(os.path.sep)[-1]
        else:
            working_dir = repo_id.split("/")[-1]

        repo_id = self._create_repo(
            repo_id, private=private, token=token, repo_url=repo_url, organization=organization
        )

        if use_temp_dir is None:
            use_temp_dir = not os.path.isdir(working_dir)

        with working_or_temp_dir(working_dir=working_dir, use_temp_dir=use_temp_dir) as work_dir:
            files_timestamps = self._get_files_timestamps(work_dir)

            # Save all files.
            self.save_pretrained(work_dir, max_shard_size=max_shard_size)
            if hasattr(self, "history") and hasattr(self, "create_model_card"):
                # This is a Keras model and we might be able to fish out its History and make a model card out of it
                base_model_card_args = {
                    "output_dir": work_dir,
                    "model_name": Path(repo_id).name,
                }
                base_model_card_args.update(base_model_card_args)
                self.create_model_card(**base_model_card_args)

            self._upload_modified_files(
                work_dir,
                repo_id,
                files_timestamps,
                commit_message=commit_message,
                token=token,
                create_pr=create_pr,
            )

    @classmethod
    def register_for_auto_class(cls, auto_class="TFAutoModel"):
        """
        Register this class with a given auto class. This should only be used for custom models as the ones in the
        library are already mapped with an auto class.

        <Tip warning={true}>

        This API is experimental and may have some slight breaking changes in the next releases.

        </Tip>

        Args:
            auto_class (`str` or `type`, *optional*, defaults to `"TFAutoModel"`):
                The auto class to register this new model with.
        """
        if not isinstance(auto_class, str):
            auto_class = auto_class.__name__

        import transformers.models.auto as auto_module

        if not hasattr(auto_module, auto_class):
            raise ValueError(f"{auto_class} is not a valid auto class.")

        cls._auto_class = auto_class


class TFConv1D(tf.keras.layers.Layer):
    """
    1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).

    Basically works like a linear layer but the weights are transposed.

    Args:
        nf (`int`):
            The number of output features.
        nx (`int`):
            The number of input features.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation to use to initialize the weights.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`.
    """

    def __init__(self, nf, nx, initializer_range=0.02, **kwargs):
        super().__init__(**kwargs)
        self.nf = nf
        self.nx = nx
        self.initializer_range = initializer_range

    def build(self, input_shape):
        if self.built:
            return
        self.built = True
        self.weight = self.add_weight(
            "weight", shape=[self.nx, self.nf], initializer=get_initializer(self.initializer_range)
        )
        self.bias = self.add_weight("bias", shape=[1, self.nf], initializer=tf.zeros_initializer())

    def call(self, x):
        bz, sl = shape_list(x)[:2]

        x = tf.reshape(x, [-1, self.nx])
        x = tf.matmul(x, self.weight) + self.bias

        x = tf.reshape(x, [bz, sl, self.nf])

        return x


class TFSharedEmbeddings(tf.keras.layers.Layer):
    r"""
    Construct shared token embeddings.

    The weights of the embedding layer is usually shared with the weights of the linear decoder when doing language
    modeling.

    Args:
        vocab_size (`int`):
            The size of the vocabulary, e.g., the number of unique tokens.
        hidden_size (`int`):
            The size of the embedding vectors.
        initializer_range (`float`, *optional*):
            The standard deviation to use when initializing the weights. If no value is provided, it will default to
            \\(1/\sqrt{hidden\_size}\\).
        kwargs (`Dict[str, Any]`, *optional*):
            Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`.
    """

    # TODO (joao): flagged for delection due to embeddings refactor

    def __init__(self, vocab_size: int, hidden_size: int, initializer_range: Optional[float] = None, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.initializer_range = hidden_size**-0.5 if initializer_range is None else initializer_range
        warnings.warn(
            "`TFSharedEmbeddings` is scheduled for deletion in v4.32, use `tf.keras.layers.Embedding` instead.",
            DeprecationWarning,
        )

    def build(self, input_shape):
        """
        Build shared token embedding layer Shared weights logic adapted from
        https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
        """
        self.weight = self.add_weight(
            "weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range)
        )
        super().build(input_shape)

    def get_config(self):
        config = {
            "vocab_size": self.vocab_size,
            "hidden_size": self.hidden_size,
            "initializer_range": self.initializer_range,
        }
        base_config = super().get_config()

        return dict(list(base_config.items()) + list(config.items()))

    def call(self, inputs: tf.Tensor, mode: str = "embedding") -> tf.Tensor:
        """
        Get token embeddings of inputs or decode final hidden state.

        Args:
            inputs (`tf.Tensor`):
                In embedding mode, should be an int64 tensor with shape `[batch_size, length]`.

                In linear mode, should be a float tensor with shape `[batch_size, length, hidden_size]`.
            mode (`str`, defaults to `"embedding"`):
               A valid value is either `"embedding"` or `"linear"`, the first one indicates that the layer should be
               used as an embedding layer, the second one that the layer should be used as a linear decoder.

        Returns:
            `tf.Tensor`: In embedding mode, the output is a float32 embedding tensor, with shape `[batch_size, length,
            embedding_size]`.

            In linear mode, the output is a float32 with shape `[batch_size, length, vocab_size]`.

        Raises:
            ValueError: if `mode` is not valid.

        Shared weights logic is adapted from
        [here](https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24).
        """
        if mode == "embedding":
            return self._embedding(inputs)
        elif mode == "linear":
            return self._linear(inputs)
        else:
            raise ValueError(f"mode {mode} is not valid.")

    def _embedding(self, input_ids):
        """Applies embedding based on inputs tensor."""
        return tf.gather(self.weight, input_ids)

    def _linear(self, inputs):
        """
        Computes logits by running inputs through a linear layer.

        Args:
            inputs: A float32 tensor with shape [..., hidden_size]

        Returns:
            float32 tensor with shape [..., vocab_size].
        """
        first_dims = shape_list(inputs)[:-1]
        x = tf.reshape(inputs, [-1, self.hidden_size])
        logits = tf.matmul(x, self.weight, transpose_b=True)

        return tf.reshape(logits, first_dims + [self.vocab_size])


class TFSequenceSummary(tf.keras.layers.Layer):
    """
    Compute a single vector summary of a sequence hidden states.

    Args:
        config ([`PretrainedConfig`]):
            The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
            config class of your model for the default values it uses):

            - **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:

                - `"last"` -- Take the last token hidden state (like XLNet)
                - `"first"` -- Take the first token hidden state (like Bert)
                - `"mean"` -- Take the mean of all tokens hidden states
                - `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
                - `"attn"` -- Not implemented now, use multi-head attention

            - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
            - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
              (otherwise to `config.hidden_size`).
            - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
              another string or `None` will add no activation.
            - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
            - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.

        initializer_range (`float`, defaults to 0.02): The standard deviation to use to initialize the weights.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`.
    """

    def __init__(self, config: PretrainedConfig, initializer_range: float = 0.02, **kwargs):
        super().__init__(**kwargs)

        self.summary_type = config.summary_type if hasattr(config, "summary_use_proj") else "last"
        if self.summary_type == "attn":
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.has_summary = hasattr(config, "summary_use_proj") and config.summary_use_proj
        if self.has_summary:
            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = tf.keras.layers.Dense(
                num_classes, kernel_initializer=get_initializer(initializer_range), name="summary"
            )

        self.has_activation = False
        activation_string = getattr(config, "summary_activation", None)
        if activation_string is not None:
            self.has_activation = True
            self.activation = get_tf_activation(activation_string)

        self.has_first_dropout = hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0
        if self.has_first_dropout:
            self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout)

        self.has_last_dropout = hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0
        if self.has_last_dropout:
            self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout)
        self.hidden_size = config.hidden_size

    def call(self, inputs, cls_index=None, training=False):
        if not isinstance(inputs, (dict, tuple, list)):
            hidden_states = inputs
        elif isinstance(inputs, (tuple, list)):
            hidden_states = inputs[0]
            cls_index = inputs[1] if len(inputs) > 1 else None
            assert len(inputs) <= 2, "Too many inputs."
        else:
            hidden_states = inputs.get("hidden_states")
            cls_index = inputs.get("cls_index", None)

        if self.summary_type == "last":
            output = hidden_states[:, -1]
        elif self.summary_type == "first":
            output = hidden_states[:, 0]
        elif self.summary_type == "mean":
            output = tf.reduce_mean(hidden_states, axis=1)
        elif self.summary_type == "cls_index":
            hidden_shape = shape_list(hidden_states)  # e.g. [batch, num choices, seq length, hidden dims]
            if cls_index is None:
                cls_index = tf.fill(
                    hidden_shape[:-2], hidden_shape[-2] - 1
                )  # A tensor full of shape [batch] or [batch, num choices] full of sequence length
            cls_shape = shape_list(cls_index)
            if len(cls_shape) <= len(hidden_shape) - 2:
                cls_index = tf.expand_dims(cls_index, axis=-1)
            # else:
            # cls_index = cls_index[..., tf.newaxis]
            # cls_index = cls_index.expand((-1,) * (cls_index.dim()-1) + (hidden_states.size(-1),))
            # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
            output = tf.gather(hidden_states, cls_index, batch_dims=len(hidden_shape) - 2)
            output = tf.squeeze(
                output, axis=len(hidden_shape) - 2
            )  # shape of output: (batch, num choices, hidden_size)
        elif self.summary_type == "attn":
            raise NotImplementedError

        if self.has_first_dropout:
            output = self.first_dropout(output, training=training)

        if self.has_summary:
            output = self.summary(output)

        if self.has_activation:
            output = self.activation(output)

        if self.has_last_dropout:
            output = self.last_dropout(output, training=training)

        return output

    def build(self, input_shape):
        if self.built:
            return
        self.built = True
        if getattr(self, "summary", None) is not None:
            with tf.name_scope("summary"):
                self.summary.build(self.hidden_size)


def get_initializer(initializer_range: float = 0.02) -> tf.keras.initializers.TruncatedNormal:
    """
    Creates a `tf.keras.initializers.TruncatedNormal` with the given range.

    Args:
        initializer_range (*float*, defaults to 0.02): Standard deviation of the initializer range.

    Returns:
        `tf.keras.initializers.TruncatedNormal`: The truncated normal initializer.
    """
    return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)