File size: 33,284 Bytes
5085882
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
""" CLAP Model

Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
Adapted to the Audio Task.
"""

from collections import OrderedDict
from dataclasses import dataclass
from email.mime import audio
from typing import Tuple, Union, Callable, Optional

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn

from .timm_model import TimmModel
import logging
from .utils import freeze_batch_norm_2d

from .pann_model import create_pann_model
from .htsat import create_htsat_model
from transformers import BertModel, RobertaModel, BartModel
from transformers.tokenization_utils_base import BatchEncoding

import json
with open('offset_pretrained_checkpoints.json', 'r') as config_file:
    config_data = json.load(config_file)

class MLPLayers(nn.Module):
    def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1):
        super(MLPLayers, self).__init__()
        self.nonlin = nonlin
        self.dropout = dropout

        sequence = []
        for u0, u1 in zip(units[:-1], units[1:]):
            sequence.append(nn.Linear(u0, u1))
            sequence.append(self.nonlin)
            sequence.append(nn.Dropout(self.dropout))
        sequence = sequence[:-2]

        self.sequential = nn.Sequential(*sequence)

    def forward(self, X):
        X = self.sequential(X)
        return X


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(
                OrderedDict(
                    [
                        ("-1", nn.AvgPool2d(stride)),
                        (
                            "0",
                            nn.Conv2d(
                                inplanes,
                                planes * self.expansion,
                                1,
                                stride=1,
                                bias=False,
                            ),
                        ),
                        ("1", nn.BatchNorm2d(planes * self.expansion)),
                    ]
                )
            )

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu(self.bn1(self.conv1(x)))
        out = self.relu(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)
        return out


class AttentionPool2d(nn.Module):
    def __init__(
        self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
    ):
        super().__init__()
        self.positional_embedding = nn.Parameter(
            torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
        )
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(
            2, 0, 1
        )  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x,
            key=x,
            value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat(
                [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
            ),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False,
        )

        return x[0]


class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self, layers, output_dim, heads, image_size=224, width=64):
        super().__init__()
        self.output_dim = output_dim
        self.image_size = image_size

        # the 3-layer stem
        self.conv1 = nn.Conv2d(
            3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.conv2 = nn.Conv2d(
            width // 2, width // 2, kernel_size=3, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.avgpool = nn.AvgPool2d(2)
        self.relu = nn.ReLU(inplace=True)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)

        self.init_parameters()

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    def init_parameters(self):
        if self.attnpool is not None:
            std = self.attnpool.c_proj.in_features**-0.5
            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
            nn.init.normal_(self.attnpool.c_proj.weight, std=std)

        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
            for name, param in resnet_block.named_parameters():
                if name.endswith("bn3.weight"):
                    nn.init.zeros_(param)

    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
        assert (
            unlocked_groups == 0
        ), "partial locking not currently supported for this model"
        for param in self.parameters():
            param.requires_grad = False
        if freeze_bn_stats:
            freeze_batch_norm_2d(self)

    def stem(self, x):
        for conv, bn in [
            (self.conv1, self.bn1),
            (self.conv2, self.bn2),
            (self.conv3, self.bn3),
        ]:
            x = self.relu(bn(conv(x)))
        x = self.avgpool(x)
        return x

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.attnpool(x)

        return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        return x.to(orig_type)


class QuickGELU(nn.Module):
    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(
            OrderedDict(
                [
                    ("c_fc", nn.Linear(d_model, d_model * 4)),
                    ("gelu", act_layer()),
                    ("c_proj", nn.Linear(d_model * 4, d_model)),
                ]
            )
        )
        self.ln_2 = LayerNorm(d_model)

    def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]

    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
        x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(
        self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU
    ):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.ModuleList(
            [
                ResidualAttentionBlock(width, heads, act_layer=act_layer)
                for _ in range(layers)
            ]
        )

    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
        for r in self.resblocks:
            x = r(x, attn_mask=attn_mask)
        return x


class VisualTransformer(nn.Module):
    def __init__(
        self,
        image_size: int,
        patch_size: int,
        width: int,
        layers: int,
        heads: int,
        output_dim: int,
        act_layer: Callable = nn.GELU,
    ):
        super().__init__()
        self.image_size = image_size
        self.output_dim = output_dim
        self.conv1 = nn.Conv2d(
            in_channels=3,
            out_channels=width,
            kernel_size=patch_size,
            stride=patch_size,
            bias=False,
        )

        scale = width**-0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(
            scale * torch.randn((image_size // patch_size) ** 2 + 1, width)
        )
        self.ln_pre = LayerNorm(width)

        self.text_branch = Transformer(width, layers, heads, act_layer=act_layer)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
        assert (
            unlocked_groups == 0
        ), "partial locking not currently supported for this model"
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, x: torch.Tensor):
        x = self.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
        x = torch.cat(
            [
                self.class_embedding.to(x.dtype)
                + torch.zeros(
                    x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
                ),
                x,
            ],
            dim=1,
        )  # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.text_branch(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        x = self.ln_post(x[:, 0, :])

        if self.proj is not None:
            x = x @ self.proj

        return x


@dataclass
class CLAPVisionCfg:
    layers: Union[Tuple[int, int, int, int], int] = 12
    width: int = 768
    patch_size: int = 16
    image_size: Union[Tuple[int, int], int] = 224
    timm_model_name: str = (
        None  # a valid model name overrides layers, width, patch_size
    )
    timm_model_pretrained: bool = (
        False  # use (imagenet) pretrained weights for named model
    )
    timm_pool: str = (
        "avg"  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
    )
    timm_proj: str = (
        "linear"  # linear projection for timm model output ('linear', 'mlp', '')
    )


# Audio Config Class
@dataclass
class CLAPAudioCfp:
    model_type: str = "PANN"
    model_name: str = "Cnn14"
    sample_rate: int = 48000
    # Param
    audio_length: int = 1024
    window_size: int = 1024
    hop_size: int = 1024
    fmin: int = 50
    fmax: int = 14000
    class_num: int = 527
    mel_bins: int = 64
    clip_samples: int = 480000


@dataclass
class CLAPTextCfg:
    context_length: int
    vocab_size: int
    width: int
    heads: int
    layers: int
    model_type: str


class CLAP(nn.Module):
    def __init__(
        self,
        embed_dim: int,
        audio_cfg: CLAPAudioCfp,
        text_cfg: CLAPTextCfg,
        quick_gelu: bool = False,
        enable_fusion: bool = False,
        fusion_type: str = "None",
        joint_embed_shape: int = 512,
        mlp_act: str = "relu",
    ):
        super().__init__()
        if isinstance(audio_cfg, dict):
            audio_cfg = CLAPAudioCfp(**audio_cfg)
        if isinstance(text_cfg, dict):
            text_cfg = CLAPTextCfg(**text_cfg)

        self.audio_cfg = audio_cfg
        self.text_cfg = text_cfg
        self.enable_fusion = enable_fusion
        self.fusion_type = fusion_type
        self.joint_embed_shape = joint_embed_shape
        self.mlp_act = mlp_act

        self.context_length = text_cfg.context_length

        # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
        # memory efficient in recent PyTorch releases (>= 1.10).
        # NOTE: timm models always use native GELU regardless of quick_gelu flag.
        act_layer = QuickGELU if quick_gelu else nn.GELU

        if mlp_act == "relu":
            mlp_act_layer = nn.ReLU()
        elif mlp_act == "gelu":
            mlp_act_layer = nn.GELU()
        else:
            raise NotImplementedError

        # audio branch
        # audio branch parameters
        if audio_cfg.model_type == "PANN":
            self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type)
        elif audio_cfg.model_type == "HTSAT":
            self.audio_branch = create_htsat_model(
                audio_cfg, enable_fusion, fusion_type
            )
        else:
            logging.error(f"Model config for {audio_cfg.model_type} not found")
            raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.")

        # text branch
        # text branch parameters
        if text_cfg.model_type == "transformer":
            self.text_branch = Transformer(
                width=text_cfg.width,
                layers=text_cfg.layers,
                heads=text_cfg.heads,
                act_layer=act_layer,
            )
            self.vocab_size = text_cfg.vocab_size
            self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
            self.positional_embedding = nn.Parameter(
                torch.empty(self.context_length, text_cfg.width)
            )
            self.ln_final = LayerNorm(text_cfg.width)
            self.text_transform = MLPLayers(
                units=[
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                ],
                dropout=0.1,
            )
            self.text_projection = nn.Sequential(
                nn.Linear(text_cfg.width, self.joint_embed_shape),
                mlp_act_layer,
                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
            )
        elif text_cfg.model_type == "bert":
            self.text_branch = BertModel.from_pretrained("/train20/intern/permanent/changli7/dataset_ptm/bert_base_uncased")
            self.text_transform = MLPLayers(
                units=[
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                ],
                dropout=0.1,
            )
            self.text_projection = nn.Sequential(
                nn.Linear(768, self.joint_embed_shape),
                mlp_act_layer,
                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
            )
        elif text_cfg.model_type == "roberta":
            self.text_branch = RobertaModel.from_pretrained(config_data["roberta-base"])
            self.text_transform = MLPLayers(
                units=[
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                ],
                dropout=0.1,
            )
            self.text_projection = nn.Sequential(
                nn.Linear(768, self.joint_embed_shape),
                mlp_act_layer,
                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
            )
        elif text_cfg.model_type == "bart":
            self.text_branch = BartModel.from_pretrained("/train20/intern/permanent/changli7/dataset_ptm/bart-base")
            self.text_transform = MLPLayers(
                units=[
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                    self.joint_embed_shape,
                ],
                dropout=0.1,
            )
            self.text_projection = nn.Sequential(
                nn.Linear(768, self.joint_embed_shape),
                mlp_act_layer,
                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
            )
        else:
            logging.error(f"Model config for {text_cfg.model_type} not found")
            raise RuntimeError(f"Model config for {text_cfg.model_type} not found.")
        self.text_branch_type = text_cfg.model_type
        # text branch parameters

        # audio branch parameters
        self.audio_transform = MLPLayers(
            units=[
                self.joint_embed_shape,
                self.joint_embed_shape,
                self.joint_embed_shape,
            ],
            dropout=0.1,
        )

        # below here is text branch parameters

        # ============================================================================================================
        self.audio_projection = nn.Sequential(
            nn.Linear(embed_dim, self.joint_embed_shape),
            mlp_act_layer,
            nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
        )

        self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
        self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
        self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False)

        self.init_text_branch_parameters()

    def init_text_branch_parameters(self):
        if self.text_branch_type == "transformer":
            nn.init.normal_(self.token_embedding.weight, std=0.02)
            nn.init.normal_(self.positional_embedding, std=0.01)
            proj_std = (self.text_branch.width**-0.5) * (
                (2 * self.text_branch.layers) ** -0.5
            )
            attn_std = self.text_branch.width**-0.5
            fc_std = (2 * self.text_branch.width) ** -0.5
            for block in self.text_branch.resblocks:
                nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
                nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
                nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
                nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
        if self.text_branch_type == "bert" or self.text_branch_type == "roberta":
            width = self.text_branch.embeddings.word_embeddings.weight.shape[-1]
        elif self.text_branch_type == "bart":
            width = self.text_branch.shared.weight.shape[-1]
        else:
            width = self.text_branch.width
        nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07))
        nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07))

        # deprecated
        # if hasattr(self.visual, 'init_parameters'):
        # self.visual.init_parameters()

        # if self.text_projection is not None:
        #     nn.init.normal_(self.text_projection, std=width**-0.5)

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    def encode_audio(self, audio, device):
        return self.audio_branch(
            audio, mixup_lambda=None, device=device
        )  # mix lambda needs to add

    # def list_of_dict_of_tensor2dict_of_tensor(self, x, device):
    #     tmp = {}
    #     for k in x[0].keys():
    #         tmp[k] = []
    #         for i in range(len(x)):
    #             tmp[k].append(x[i][k][:77])
    #     for k in x[0].keys():
    #         tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True)
    #     return tmp

    def encode_text(self, text, device):
        if self.text_branch_type == "transformer":
            text = text.to(device=device, non_blocking=True)
            x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]

            x = x + self.positional_embedding
            x = x.permute(1, 0, 2)  # NLD -> LND
            x = self.text_branch(x, attn_mask=self.attn_mask)
            x = x.permute(1, 0, 2)  # LND -> NLD
            x = self.ln_final(x)

            # x.shape = [batch_size, n_ctx, transformer.width]
            # take features from the eot embedding (eot_token is the highest number in each sequence)
            x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)])
        elif self.text_branch_type == "bert":
            # text = self.list_of_dict_of_tensor2dict_of_tensor(text, device)
            # text = BatchEncoding(text)
            x = self.text_branch(
                input_ids=text["input_ids"].to(device=device, non_blocking=True),
                attention_mask=text["attention_mask"].to(
                    device=device, non_blocking=True
                ),
                token_type_ids=text["token_type_ids"].to(
                    device=device, non_blocking=True
                ),
            )["pooler_output"]
            x = self.text_projection(x)
        elif self.text_branch_type == "roberta":
            x = self.text_branch(
                input_ids=text["input_ids"].to(device=device, non_blocking=True),
                attention_mask=text["attention_mask"].to(
                    device=device, non_blocking=True
                ),
            )["pooler_output"]
            x = self.text_projection(x)
        elif self.text_branch_type == "bart":
            x = torch.mean(
                self.text_branch(
                    input_ids=text["input_ids"].to(device=device, non_blocking=True),
                    attention_mask=text["attention_mask"].to(
                        device=device, non_blocking=True
                    ),
                )["encoder_last_hidden_state"],
                axis=1,
            )
            x = self.text_projection(x)
        else:
            logging.error(f"Model type {self.text_branch_type} not found")
            raise RuntimeError(f"Model type {self.text_branch_type} not found.")
        return x

    def forward(self, audio, text, device=None):
        """Forward audio and text into the CLAP

        Parameters
        ----------
        audio: torch.Tensor (batch_size, audio_length)
            the time-domain audio input / the batch of mel_spec and longer list.
        text: torch.Tensor () // need to add
            the text token input
        """
        if device is None:
            if audio is not None:
                device = audio.device
            elif text is not None:
                device = text.device
        if audio is None and text is None:
            # a hack to get the logit scale
            return self.logit_scale_a.exp(), self.logit_scale_t.exp()
        elif audio is None:
            return self.encode_text(text, device=device)
        elif text is None:
            return self.audio_projection(
                self.encode_audio(audio, device=device)["embedding"]
            )
        audio_features = self.audio_projection(
            self.encode_audio(audio, device=device)["embedding"]
        )
        audio_features = F.normalize(audio_features, dim=-1)

        text_features = self.encode_text(text, device=device)
        # print("text_features", text_features)
        # print("text_features.shape", text_features.shape)
        # print("text_features.type", type(text_features))
        text_features = F.normalize(text_features, dim=-1)

        audio_features_mlp = self.audio_transform(audio_features)
        text_features_mlp = self.text_transform(text_features)
        # Four outputs: audio features (basic & MLP), text features (basic & MLP)
        return (
            audio_features,
            text_features,
            audio_features_mlp,
            text_features_mlp,
            self.logit_scale_a.exp(),
            self.logit_scale_t.exp(),
        )

    def get_logit_scale(self):
        return self.logit_scale_a.exp(), self.logit_scale_t.exp()

    def get_text_embedding(self, data):
        """Get the text embedding from the model

        Parameters
        ----------
        data: torch.Tensor
            a tensor of text embedding

        Returns
        ----------
        text_embed: torch.Tensor
            a tensor of text_embeds (N, D)

        """
        device = next(self.parameters()).device
        for k in data:
            data[k] = data[k].to(device)
        text_embeds = self.encode_text(data, device=device)
        text_embeds = F.normalize(text_embeds, dim=-1)

        return text_embeds

    def get_audio_embedding(self, data):
        """Get the audio embedding from the model

        Parameters
        ----------
        data: a list of dict
            the audio input dict list from 'get_audio_feature' method

        Returns
        ----------
        audio_embed: torch.Tensor
            a tensor of audio_embeds (N, D)

        """
        device = next(self.parameters()).device
        # input_dict = {}
        # keys = data[0].keys()
        # for k in keys:
        #     input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(
        #         device
        #     )
        audio_embeds = self.audio_projection(
            self.encode_audio(data, device=device)["embedding"]
        )
        audio_embeds = F.normalize(audio_embeds, dim=-1)

        return audio_embeds

    def audio_infer(self, audio, hopsize=None, device=None):
        """Forward one audio and produce the audio embedding

        Parameters
        ----------
        audio:  (audio_length)
            the time-domain audio input, notice that it must be only one input
        hopsize: int
            the overlap hopsize as the sliding window

        Returns
        ----------
        output_dict: {
            key: [n, (embedding_shape)] if "HTS-AT"
            or
            key: [(embedding_shape)] if "PANN"
        }
            the list of key values of the audio branch

        """

        assert not self.training, "the inference mode must be run at eval stage"
        output_dict = {}
        # PANN
        if self.audio_cfg.model_type == "PANN":
            audio_input = audio.unsqueeze(dim=0)
            output_dict[key] = self.encode_audio(audio_input, device=device)[
                key
            ].squeeze(dim=0)
        elif self.audio_cfg.model_type == "HTSAT":
            # repeat
            audio_len = len(audio)
            k = self.audio_cfg.clip_samples // audio_len
            if k > 1:
                audio = audio.repeat(k)
                audio_len = len(audio)

            if hopsize is None:
                hopsize = min(hopsize, audio_len)

            if audio_len > self.audio_cfg.clip_samples:
                audio_input = [
                    audio[pos : pos + self.audio_cfg.clip_samples].clone()
                    for pos in range(
                        0, audio_len - self.audio_cfg.clip_samples, hopsize
                    )
                ]
                audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
                audio_input = torch.stack(audio_input)
                output_dict[key] = self.encode_audio(audio_input, device=device)[key]
            else:
                audio_input = audio.unsqueeze(dim=0)
                output_dict[key] = self.encode_audio(audio_input, device=device)[
                    key
                ].squeeze(dim=0)

        return output_dict


def convert_weights_to_fp16(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
            l.weight.data = l.weight.data.half()
            if l.bias is not None:
                l.bias.data = l.bias.data.half()

        if isinstance(l, nn.MultiheadAttention):
            for attr in [
                *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
                "in_proj_bias",
                "bias_k",
                "bias_v",
            ]:
                tensor = getattr(l, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ["text_projection", "proj"]:
            if hasattr(l, name):
                attr = getattr(l, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)


# Ignore the state dict of the vision part
def build_model_from_openai_state_dict(
    state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = "None"
):
    embed_dim = model_cfg["embed_dim"]
    audio_cfg = model_cfg["audio_cfg"]
    text_cfg = model_cfg["text_cfg"]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(
        set(
            k.split(".")[2]
            for k in state_dict
            if k.startswith(f"transformer.resblocks")
        )
    )

    audio_cfg = CLAPAudioCfp(**audio_cfg)
    text_cfg = CLAPTextCfg(**text_cfg)

    model = CLAP(
        embed_dim,
        audio_cfg=audio_cfg,
        text_cfg=text_cfg,
        quick_gelu=True,  # OpenAI models were trained with QuickGELU
        enable_fusion=enable_fusion,
        fusion_type=fusion_type,
    )
    state_dict["logit_scale_a"] = state_dict["logit_scale"]
    state_dict["logit_scale_t"] = state_dict["logit_scale"]
    pop_keys = list(state_dict.keys())[::]
    # pop the visual branch saved weights
    for key in pop_keys:
        if key.startswith("visual."):
            state_dict.pop(key, None)

    for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]:
        state_dict.pop(key, None)

    # not use fp16
    # convert_weights_to_fp16(model)
    model.load_state_dict(state_dict, strict=False)
    return model.eval()


def trace_model(model, batch_size=256, device=torch.device("cpu")):
    model.eval()
    audio_length = model.audio_cfg.audio_length
    example_audio = torch.ones((batch_size, audio_length), device=device)
    example_text = torch.zeros(
        (batch_size, model.context_length), dtype=torch.int, device=device
    )
    model = torch.jit.trace_module(
        model,
        inputs=dict(
            forward=(example_audio, example_text),
            encode_text=(example_text,),
            encode_image=(example_audio,),
        ),
    )
    model.audio_cfg.audio_length = audio_length  # Question: what does this do?
    return model