`idZddlmZddlZddlZddlZddlmZddlm Z ddl m Z ddl m Z mZddlZdd lmZdd lmZmZmZmZmZmZdd lmZdd lmZmZmZm Z m!Z!m"Z"d dl#m$Z$e!j%e&Z'dZ(dZ)gdZ*dZ+dZ,e GddeZ-e GddeZ.e GddeZ/e GddeZ0d`d Z1dad$Z2 dbdcd.Z3Gd/d0ej4j5Z6Gd1d2ej4j5Z7Gd3d4ej4j5Z8Gd5d6ej4j5Z9Gd7d8ej4j5Z:Gd9d:ej4j5Z;Gd;dej4j5Z=Gd?d@ej4j5Z>GdAdBej4j5Z?GdCdDej4j5Z@GdEdFej4j5ZAGdGdHeZBdIZCdJZDdddMZEGdNdOej4j5ZFeGdPdQej4j5ZGedReCGdSdTeBZHGdUdVej4j5ZIGdWdXej4j5ZJedYeCGdZd[eBZKed\eCGd]d^eBeZLgd_ZMdS)ezTF 2.0 Swin Transformer model.) annotationsN)Iterable) dataclass)partial)AnyCallable)ACT2FN)TFPreTrainedModelTFSequenceClassificationLossget_initializerkeraskeras_serializable unpack_inputs) shape_list) ModelOutputadd_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings) SwinConfigrzµsoft/swin-tiny-patch4-window7-224)r1iztabby, tabby catcLeZdZUdZdZded<dZded<dZded<dZded<dS) TFSwinEncoderOutputaH Swin encoder's outputs, with potential hidden states and attentions. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Ntf.Tensor | Nonelast_hidden_statetuple[tf.Tensor, ...] | None hidden_states attentionsreshaped_hidden_states) __name__ __module__ __qualname____doc__r__annotations__r r!r"}/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/swin/modeling_tf_swin.pyrrDsd2+/....26M6666/3J3333;???????r)rcZeZdZUdZdZded<dZded<dZded<dZded<dZ ded <dS) TFSwinModelOutputa Swin model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed): Average pooling of the last layer hidden-state. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nrr pooler_outputrr r!r") r#r$r%r&rr'r-r r!r"r(r)r*r,r,esu6+/....&*M****26M6666/3J3333;???????r)r,cpeZdZUdZdZded<dZded<dZded<dZded<dZ ded <e d Z dS) TFSwinMaskedImageModelingOutputa Swin masked image model outputs. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided): Masked image modeling (MLM) loss. reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Reconstructed pixel values. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nrlossreconstructionrr r!r"cDtjdt|jS)Nzlogits attribute is deprecated and will be removed in version 5 of Transformers. Please use the reconstruction attribute to retrieve the final output instead.)warningswarn FutureWarningr1selfs r*logitsz&TFSwinMaskedImageModelingOutput.logitss*  ]    ""r)) r#r$r%r&r0r'r1r r!r"propertyr8r(r)r*r/r/s6"D!!!!'+N++++26M6666/3J3333;????? ##X###r)r/cZeZdZUdZdZded<dZded<dZded<dZded<dZ ded <dS) TFSwinImageClassifierOutputa Swin outputs for image classification. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nrr0r8rr r!r") r#r$r%r&r0r'r8r r!r"r(r)r*r;r;st6"D!!!!#F####26M6666/3J3333;???????r)r; input_feature tf.Tensor window_sizeintreturnc t|\}}}}tj||||z|||z||f}tj|d}tj|d|||f}|S)z2 Partitions the given input into windows. rrr )rtfreshape transpose)r<r> batch_sizeheightwidth num_channelswindowss r*window_partitionrOs|/9.G.G+J|J V{*K+9M{\hiMl=*<==Gj2{K"NOOG Nr)rNrKrLc htj|d}tj||z||zz tj}tj||}tj||||z||z||df}tj|d}tj||||df}|S)z? Merges windows to produce higher resolution features. rrFrB)rGshapecastint32mathfloordivrHrI)rNr>rKrLxyrJs r*window_reverserXs !A + ";)rsepsilondropoutr()super__init__TFSwinPatchEmbeddingsrq num_patches grid_size patch_grid embed_dimrnuse_absolute_embeddingsrlayersLayerNormalizationrtDropouthidden_dropout_probrwrm)r7rmrnkwargs __class__s r*ryzTFSwinEmbeddings.__init__s""6""" 5fCU V V V0</9),'-'E$L333NN |++F,FY+WW  r)rgtf.TensorShapecl|jr&|dd|jfdd|_nd|_|jr.|d|jdz|jfdd|_nd|_|jrdSd|_t|ddPtj |j j 5|j ddddn #1swxYwYt|d d]tj |jj 5|j dd|jjgdddn #1swxYwYt|d dStj |jj 5|j dddddS#1swxYwYdSdS) Nrzeros mask_tokenrQ initializerrspositional_embeddings)rrsTrqrtrw)rn add_weightr~rrr{position_embeddingsbuiltgetattrrG name_scoperqrsbuildrtrmrwr7rgs r*rzTFSwinEmbeddings.builds   #"ooQ4>4JX_frossDOO"DO  ' ,'+D$q($.9wUl(7((D $ $(,D $ :  F 4+T 2 2 >t49:: 2 2%++D111 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 & & 2ty~.. E E tT[-B CDDD E E E E E E E E E E E E E E E 4D ) ) 5t|011 ) ) ""4((( ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) 6 5s60CCC(EE  E ?F''F+.F+N pixel_valuesr=bool_masked_pos bool | Noner]!tuple[tf.Tensor, tuple[int, int]]c|||\}}|||}t|\}}}|ntj|j|d} tj| |d} tj|d} tj| | j} |d| z z| | zz}|j ||j z}| ||}||fS)Nr]rrrFra) rqrtrrGrepeatr expand_dimsrRdtyperrw) r7rrr] embeddingsoutput_dimensionsrJseq_len_ mask_tokensmasks r*callzTFSwinEmbeddings.call6s)-(=(=lU](=(^(^% %YYzHY== !+J!7!7 GQ  &)DOZCCK)K!</266D74!233D#sTz2[45GGJ  # /#d&>>J\\*x\@@ ,,,r)F)rmrrnr^r@rorgrr@ro)NF)rr=rrr]r^r@r)r#r$r%r&ryrr __classcell__rs@r*rlrl s       ))))6^c---------r)rlc<eZdZdZfdZdd ZdddZddZxZS)rzz# Image to Patch Embedding. c 8tjdi||j|j}}|j|j}}t |tjj r|n||f}t |tjj r|n||f}|d|dz|d|dzz}||_||_||_||_ |d|dz|d|dzf|_ tj ||j|jdd|_dS)Nrrvalid projection)filters kernel_sizestridespaddingrsr()rxry image_size patch_sizerMr~ isinstance collectionsabcrr{r|rrConv2Dr) r7rmrrrrM hidden_sizer{rs r*ryzTFSwinPatchEmbeddings.__init__Ss%""6"""!'!2F4EJ $*$79Ik #-j+/:R#S#SqZZZdfpYq #-j+/:R#S#SqZZZdfpYq !!} 1 5*Q-:VW=:XY $$(&$Q-:a=8*Q-:VW=:XY,--O .  r)rr=rKr?rLr@c:||jdzdkr9dddd|jd||jdzz ff}tj||}||jdzdkr9ddd|jd||jdzz fdf}tj||}|S)Nrrrr)rrGpad)r7rrKrL pad_valuess r* maybe_padzTFSwinPatchEmbeddings.maybe_padhs 4?1% % * * &&1doa6H5SWSbcdSeKe6e2fgJ6, ;;L DOA& &! + + &1doa.@6DO\]L^C^.^*_aghJ6, ;;Lr)Fr]r^rct|\}}}}tjr||jkrt d||||}tj|d}|||}tj|d}t|\}} }}||f} tj||| df}tj|d}|| fS)NzeMake sure that the channel dimension of the pixel values match with the one set in the configuration.rrCr rrrr rrCrFrrCr) rrGexecuting_eagerlyrM ValueErrorrrIrrH) r7rr]rrMrKrLrrJchannelsrs r*rzTFSwinPatchEmbeddings.callqs)3L)A)A&<  ! ! ld6G&G&Gw ~~lFEBB |L,?? __\H_EE \*l;; .8.D.D+ Hfe#UOZ Z2,FGG \*i88 ,,,r)Nc|jrdSd|_t|dd\tj|jj5|jddd|jgddddS#1swxYwYdSdS)NTr)rrrGrrrsrrMrs r*rzTFSwinPatchEmbeddings.builds :  F 4t , , 8t344 M M%%tT49J&KLLL M M M M M M M M M M M M M M M M M M 9 8s$A11A58A5)rr=rKr?rLr?r@r=r)rr=r]r^r@rN r#r$r%r&ryrrrrrs@r*rzrzNs     *-----0MMMMMMMMr)rzcBeZdZdZ ddfd ZddZdddZddZxZS)TFSwinPatchMergingaB Patch Merging Layer. Args: input_resolution (`tuple[int]`): Resolution of input feature. dim (`int`): Number of input channels. norm_layer (`keras.layer.Layer`, *optional*, defaults to `keras.layers.LayerNormalization`): Normalization layer class. Ninput_resolutiontuple[int, int]dimr? norm_layerCallable | Noner@roc  tjd i|||_||_tjd|zdd|_|(tjdd|_ dS|d|_ dS) NrCF reduction)use_biasrsrurtrvrsrrr() rxryrrrrDenserrrt)r7rrrrrs r*ryzTFSwinPatchMerging.__init__s ""6""" 0++AGe++VV   7767RRDIII" ///DIIIr)r<r=rKrLcx|dzdkp|dzdk}|r%dd|dzfd|dzfdf}tj||}|S)NrCrrr)rGr)r7r<rKrL should_padrs r*rzTFSwinPatchMerging.maybe_padsYqjAo:519>  > 1fqj/Auqy>6JJF=*==Mr)Finput_dimensionsr]r^c |\}}t|\}}}tj|||||f}||||}|ddddddddddf} |ddddddddddf} |ddddddddddf} |ddddddddddf} tj| | | | gd}tj||dd|zf}|||}|||}|S)NrrCrrFrDr)rrGrHrconcatrtr) r7r<rr]rKrLrJrrMinput_feature_0input_feature_1input_feature_2input_feature_3s r*rzTFSwinPatchMerging.callsZ( &0&?&?# A| =:vul2[\\ }feDD '14a4Aqqq(89'14a4Aqqq(89'14a4Aqqq(89'14a4Aqqq(89 ?O_Ve"fhjkk  JA ,<=   -( CC }xHH r)c|jrdSd|_t|dd[tj|jj5|jddd|jzgdddn #1swxYwYt|dd^tj|jj5|jddd|jzgddddS#1swxYwYdSdS)NTrrDrt) rrrGrrrsrrrtrs r*rzTFSwinPatchMerging.builds{ :  F 4d + + 7t~233 A A$$dD!dh,%?@@@ A A A A A A A A A A A A A A A 4 & & 2ty~.. < < tQ\ :;;; < < < < < < < < < < < < < < < < < < 3 2s$&A22A69A6,&CC#&C#r)rrrr?rrr@ro)r<r=rKr?rLr?r@r=r)r<r=rrr]r^r@r=rrs@r*rrs  Z^ 0 0 0 0 0 0 06 < < < < < < <