T^(h7fdZddlmZddlZddlZddlZddlmZddl m Z ddl m Z m Z mZmZmZmZmZmZddlZddlmZdd lmZmZmZmZmZmZdd lm Z dd l!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'd d l(m)Z)e&jTe+Z,dZ-dZ.gdZ/dZ0dZ1eGdde"Z2eGdde"Z3eGdde"Z4eGdde"Z5dLdZ6dMdZ7 dN dOdZ8GddejrjtZ;Gdd ejrjtZ<Gd!d"ejrjtZ=Gd#d$ejrjtZ>Gd%d&ejrjtZ?Gd'd(ejrjtZ@Gd)d*ejrjtZAGd+d,ejrjtZBGd-d.ejrjtZCGd/d0ejrjtZDGd1d2ejrjtZEGd3d4ejrjtZFGd5d6eZGd7ZHd8ZIdPd9ZJGd:d;ejrjtZKeGd<d=ejrjtZLe$d>eHGd?d@eGZMGdAdBejrjtZNGdCdDejrjtZOe$dEeHGdFdGeGZPe$dHeHGdIdJeGeZQgdKZRy)QzTF 2.0 Swin Transformer model.) annotationsN) dataclass)partial)AnyCallableDictIterableListOptionalTupleUnion)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 catcJeZdZUdZdZded<dZded<dZded<dZded<y) 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. NOptional[tf.Tensor]last_hidden_stateTuple[tf.Tensor, ...] | None hidden_states attentionsreshaped_hidden_states) __name__ __module__ __qualname____doc__r#__annotations__r%r&r'g/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/swin/modeling_tf_swin.pyr!r!Cs72.2*126M/6/3J,3;?8?r.r!cXeZdZUdZdZded<dZded<dZded<dZded <dZ ded <y) 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. Nr"r#tf.Tensor | None pooler_outputr$r%r&r') r(r)r*r+r#r,r3r%r&r'r-r.r/r1r1dsB6.2*1&*M#*26M/6/3J,3;?8?r.r1cheZdZUdZdZded<dZded<dZded<dZded <dZ ded <e d Z y) TFSwinMaskedImageModelingOutputa Swin masked image model outputs. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided): Masked image modeling (MLM) loss. reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Reconstructed pixel values. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nr2lossr"reconstructionr$r%r&r'cNtjdt|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 FutureWarningr7selfs r/logitsz&TFSwinMaskedImageModelingOutput.logitss%  ]  """r.) r(r)r*r+r6r,r7r%r&r'propertyr>r-r.r/r5r5sS6"D !*.N'.26M/6/3J,3;?8? ##r.r5cXeZdZUdZdZded<dZded<dZded<dZded <dZ ded <y) 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. Nr2r6r"r>r$r%r&r') r(r)r*r+r6r,r>r%r&r'r-r.r/rArAsA6"D !"&F &26M/6/3J,3;?8?r.rAc t|\}}}}tj||||z|||z||f}tj|d}tj|d|||f}|S)z2 Partitions the given input into windows. rrr)rtfreshape transpose) input_feature window_size batch_sizeheightwidth num_channelswindowss r/window_partitionrRst/9.G+J|JJ V{*K+9M{\hiMll=*<=Gjj2{K"NOG Nr.c ttj|d}tj||z||zz tj}tjj ||}tj ||||z||z||df}tj|d}tj ||||df}|S)z? Merges windows to produce higher resolution features. rrGrC)rHshapecastint32mathfloordivrIrJ)rQrLrNrOxyrMs r/window_reverser[s !A + ";)rqepsilondropoutr-)super__init__TFSwinPatchEmbeddingsro num_patches grid_size patch_grid embed_dimuse_mask_tokenuse_absolute_embeddingsrlayersLayerNormalizationrrDropouthidden_dropout_probruconfig)r=rr}kwargs __class__s r/rwzTFSwinEmbeddings.__init__s "6" 5fCU V00<<//99)),'-'E'E$LL333N ||++F,F,FY+W  r.c|jr'|jdd|jfdd|_nd|_|jr4|jd|j dz|jfdd|_nd|_|jryd|_t|ddMtj|jj5|jjddddt|d ddtj|jj5|jjdd|jjgdddt|d dNtj|j j5|j jddddyy#1swYxYw#1swYqxYw#1swYyxYw) Nrzeros mask_tokenrT initializerrqpositional_embeddings)rrqTrorrru)r} add_weightr|rr~ryposition_embeddingsbuiltgetattrrH name_scoperorqbuildrrrrur=rhs r/rzTFSwinEmbeddings.builds   "ooQ4>>4JX_frosDO"DO  ' ''+D$$q($..9wUl(7(D $(,D $ ::  4+T 2 >t4499: 2%%++D1 2 4 & 2tyy~~. E tT[[-B-B CD E 4D ) 5t||001 ) ""4( ) ) 6  2 2 E E ) )s$ F=&3G G=G GGc|j||\}}|j||}t|\}}}||tj|j |d} tj| |d} tj |d} tj| | j} |d| z z| | zz}|j||jz}|j||}||fS)NrerrrGr^) rorrrrHrepeatr expand_dimsrUdtyperru) r= pixel_valuesbool_masked_posre embeddingsoutput_dimensionsrMseq_len_ mask_tokensmasks r/callzTFSwinEmbeddings.call5s)-(=(=lU](=(^% %YYzHY= !+J!7 GQ  &))DOOZCK))K!>/26D774!2!23D#sTz2[45GGJ  # # /#d&>&>>J\\*x\@ ,,,r.F)rrr}boolreturnNonerhtf.TensorShaperr)NF)r tf.TensorrOptional[bool]rerr!Tuple[tf.Tensor, Tuple[int, int]])r(r)r*r+rwrr __classcell__rs@r/rmrm s> )6af-%-8F-Y]- *-r.rmc<eZdZdZfdZddZdddZd dZxZS) rxz# Image to Patch Embedding. c Bt|di||j|j}}|j|j }}t |tjjr|n||f}t |tjjr|n||f}|d|dz|d|dzz}||_||_||_||_ |d|dz|d|dzf|_ tjj||j|jdd|_y)Nrrvalid projection)filters kernel_sizestridespaddingrqr-)rvrw image_size patch_sizerPr| isinstance collectionsabcr ryrzrrConv2Dr) r=rrrrrP hidden_sizeryrs r/rwzTFSwinPatchEmbeddings.__init__Rs "6"!'!2!2F4E4EJ $*$7$79I9Ik #-j+//:R:R#SZZdfpYq #-j+//:R:R#SZZdfpYq !!} 1 5*Q-:VW=:XY $$(&$Q-:a=8*Q-:VW=:XY,,--OO . r.cR||jdzdk7r>dddd|jd||jdzz ff}tj||}||jdzdk7r>ddd|jd||jdzz fdf}tj||}|S)Nrrrr)rrHpad)r=rrNrO pad_valuess r/ maybe_padzTFSwinPatchEmbeddings.maybe_padgs 4??1% % * &&1dooa6H5SWSbSbcdSeKe6e2fgJ66, ;L DOOA& &! + &1dooa.@6DOO\]L^C^.^*_aghJ66, ;Lr.ct|\}}}}tjr||jk7r t d|j |||}tj |d}|j||}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.rrDrrrrrrrDrGrrDr) rrHexecuting_eagerlyrP ValueErrorrrJrrI) r=rrerrPrNrOrrMchannelsrs r/rzTFSwinPatchEmbeddings.callps)3L)A&<   !ld6G6G&Gw ~~lFEB ||L,? __\H_E \\*l; .8.D+ Hfe#UOZZ Z2,FG \\*i8 ,,,r.c|jryd|_t|dd\tj|jj 5|jj ddd|jgdddyy#1swYyxYw)NTr)rrrHrrrqrrPrs r/rzTFSwinPatchEmbeddings.builds} ::  4t , 8t334 M%%tT49J9J&KL M M 9 M Ms *A??B)rrrNintrOrrrr)rrrerrrN r(r)r*r+rwrrrrrs@r/rxrxMs *-0Mr.rxcPeZdZdZ d dfd ZddZd d dZddZxZS) 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. c t|d i|||_||_tj j d|zdd|_|'tj jdd|_ y|d|_ y) NrDF reduction)use_biasrqrsrrrtrqrpr-) rvrwinput_resolutiondimrrDenserrrr)r=rr norm_layerrrs r/rwzTFSwinPatchMerging.__init__sr "6" 0++AGe++V   7767RDI"/DIr.cz|dzdk(xs|dzdk(}|r&dd|dzfd|dzfdf}tj||}|S)NrDrrr)rHr)r=rKrNrO should_padrs r/rzTFSwinPatchMerging.maybe_padsSqjAo:519>  1fqj/Auqy>6JJFF=*=Mr.c|\}}t|\}}}tj|||||f}|j|||}|ddddddddddf} |ddddddddddf} |ddddddddddf} |ddddddddddf} tj| | | | gd}tj||dd|zf}|j ||}|j ||}|S)NrrDrrGrEr)rrHrIrconcatrrr) r=rKinput_dimensionsrerNrOrMrrPinput_feature_0input_feature_1input_feature_2input_feature_3s r/rzTFSwinPatchMerging.calls)( &0&?# A| =:vul2[\ }feD '14a4Aq(89'14a4Aq(89'14a4Aq(89'14a4Aq(89 ?O_Ve"fhjk  JA ,<=  -( C }xH r.c|jryd|_t|dd]tj|jj 5|jj ddd|jzgdddt|dd^tj|jj 5|jj ddd|jzgdddyy#1swYuxYw#1swYyxYw)NTrrErr) rrrHrrrqrrrrrs r/rzTFSwinPatchMerging.builds ::  4d + 7t~~223 A$$dD!dhh,%?@ A 4 & 2tyy~~. < tQ\ :; < < 3 A A <