sgΑ,dZddlmZddlZddlZddlmZmZm Z ddl Z ddl Z ddlmZddlmZmZmZddlmZmZmZmZmZmZmZdd lmZmZdd lm Z m!Z!m"Z"m#Z#d d l$m%Z%e#jLe'Z(d Z)dZ*gdZ+dZ,dZ-Gddej\j^Z0Gddej\j^Z1Gddej\j^Z2Gddej\j^Z3Gddej\j^Z4Gddej\j^Z5Gddej\j^Z6Gd d!ej\j^Z7Gd"d#ej\j^Z8eGd$d%ej\j^Z9Gd&d'eZ:d(Z;d)Z<e!d*e;Gd+d,e:Z=Gd-d.ej\j^Z>e!d/e;Gd0d1e:eZ?y)2zTF 2.0 ViT model.) annotationsN)OptionalTupleUnion)get_tf_activation)TFBaseModelOutputTFBaseModelOutputWithPoolingTFSequenceClassifierOutput)TFModelInputTypeTFPreTrainedModelTFSequenceClassificationLossget_initializerkeraskeras_serializable unpack_inputs) shape_liststable_softmax)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging) ViTConfigrz!google/vit-base-patch16-224-in21k)rizgoogle/vit-base-patch16-224z Egyptian catcNeZdZdZdfd ZddZddZ d d dZxZS) TFViTEmbeddingszB Construct the CLS token, position and patch embeddings. c t|di|t|d|_tj j |j|_||_ y)Npatch_embeddingsnamerate) super__init__TFViTPatchEmbeddingsrrlayersDropouthidden_dropout_probdropoutconfigselfr,kwargs __class__s Z/var/www/html/venv/lib/python3.12/site-packages/transformers/models/vit/modeling_tf_vit.pyr&zTFViTEmbeddings.__init__>sI "6" 4VBT U||++1K1K+L  cd|jj}|jdd|jjft |jj dd|_|jd|dz|jjft |jj dd|_|jryd|_ t|ddNtj|jj5|jjddddyy#1swYyxYw)NrT cls_token)shape initializer trainabler!position_embeddingsr)r num_patches add_weightr, hidden_sizerinitializer_ranger4r8builtgetattrtf name_scoper!build)r. input_shaper9s r1rAzTFViTEmbeddings.buildEs ++77 a001' (E(EF )  $(??kAot{{'>'>?' (E(EF& $3$   ::  4+T 2 >t4499: 2%%++D1 2 2 ? 2 2s D&&D/c t|\}}}|dz }t|j\}} }| dz} || k(r||k(r |jS|jddddf} |jddddf} ||jjz} ||jjz} tj j t j| dttj| ttj| |f| | fd} t| }| |dk(r| |dk(sJt j| dd|f } t j| | fd S) a# This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. 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M *!,M !/=3C2E!E F 1]4D D h]4E~$Vhh h +;LYg  r2c|jryd|_t|ddK|jD];}tj|j 5|j dddd=yy#1swYIxYw)NTr)r=r>rr?r@r!rA)r.rBrs r1rAzTFViTEncoder.build#sp ::  4$ ' 3 &]]5::.&KK%&& & 4&&s A..A7 rlr)rrorrorrrrrrrrrrerrrnz*Union[TFBaseModelOutput, Tuple[tf.Tensor]]rmrrxs@r1r r s]h$  $ $  $ # $  $ $  4$ L&r2r c|eZdZeZddfd ZddZdZe d d dZ d dZ xZ S) TFViTMainLayerc t|di|||_t|d|_t |d|_tjj|jd|_ |rt|d|_ yd|_ y)NrWr encoder layernormrpoolerr$)r%r&r,rrWr r%rr(rrr& TFViTPoolerr')r.r,add_pooling_layerr/r0s r1r&zTFViTMainLayer.__init__1so "6" )&|D#F; 88AVAV]h8is "!r2c| td|j|||}|tdg|jjz}|j ||||||} | d} |j | } |j|j| nd} |s | | f| ddzSt| | | j| jS) Nz You have to specify pixel_values)rhrcre)rrrrrrerrrr)r pooler_outputrr) rrWrr,rr%r&r'r rr) r.rhrrrrcrreembedding_outputencoder_outputssequence_output pooled_outputs r1rkzTFViTMainLayer.callEs  ?@ @??%%=+   % %!>!>>I,,*/!5# ' *!,...@FJkkF] / Bcg #]3oab6II I+-')77&11   r2c|jryd|_t|ddMtj|jj 5|jj ddddt|ddMtj|jj 5|jj ddddt|dddtj|jj 5|jj dd|jjgdddt|ddNtj|jj 5|jj ddddyy#1swY1xYw#1swYxYw#1swY~xYw#1swYyxYw)NTrWr%r&r') r=r>r?r@rWr!rAr%r&r,r;r'rs r1rAzTFViTMainLayer.buildzsb ::  4t , 8t334 ,%%d+ , 4D ) 5t||001 ) ""4( ) 4d + 7t~~223 L$$dD$++2I2I%JK L 44 ( 4t{{//0 ( !!$' ( ( 5 , , ) ) L L ( (s0 F%F#?3F/0F;F #F,/F8;G)T)r,rr)rr)rnzkeras.layers.LayerNNNNNNFrhTFModelInputType | Nonernp.ndarray | tf.Tensor | NonerOptional[bool]rr:rcr:rr:rerrrnz5Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]rm) rsrtrur config_classr&r+r.rrkrArwrxs@r1r#r#-sLX0"1537,0/337&*2 -2 12 * 2 - 2 #1 2 $2 2  ?2 2 h(r2r#ceZdZdZeZdZdZy)TFViTPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. vitrhN)rsrtrurvrr;base_model_prefixmain_input_namer$r2r1r=r=s L$Or2r=a This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! Args: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. ar Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False``): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). z]The bare ViT Model transformer outputting raw hidden-states without any specific head on top.c eZdZdddfdZeeeeee e de  d d dZ d dZ xZS) TFViTModelT)r)cRt||g|i|t||d|_y)Nr>r)r!)r%r&r#r>)r.r,r)rr/r0s r1r&zTFViTModel.__init__s, 3&3F3!&) r.rhrrrrcrrers r1rkzTFViTModel.calls3&((%/!5%=# r2c|jryd|_t|ddNtj|jj 5|jj ddddyy#1swYyxYw)NTr>)r=r>r?r@r>r!rArs r1rAzTFViTModel.build se ::  4 % 1txx}}- %t$ % % 2 % %s A11A:rlr6r7rm)rsrtrur&rrVIT_INPUTS_DOCSTRINGr_CHECKPOINT_FOR_DOCr _CONFIG_FOR_DOC_EXPECTED_OUTPUT_SHAPErkrArwrxs@r1rBrBs FJ[ *+?@&0$. 1537,0/337&*-1*  -  #1 $ ?A,%r2rBc0eZdZdfd ZddZddZxZS)r(c t|di|tjj |j t |jdd|_||_ y)Ntanhr)rr activationr!r$) r%r&rr(rr;rr<rr,r-s r1r&zTFViTPooler.__init__sT "6"\\''$$.v/G/GH (   r2c<|dddf}|j|}|S)Nrr)r)r.rfirst_token_tensorr4s r1rkzTFViTPooler.calls*+1a40 *< = r2c(|jryd|_t|ddetj|jj 5|jj dd|jjgdddyy#1swYyxYwrrrs r1rAzTFViTPooler.build&rrrlrrmrrxs@r1r(r(s Hr2r(a ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution. ceZdZdfd Zeeeeee e e  d ddZ ddZ xZS) TFViTForImageClassificationc t||g|i||j|_t|dd|_t j j|jt|jd|_ ||_ y)NFr>rD classifierr) r%r& num_labelsr#r>rr(rrr<r[r,)r.r,rr/r0s r1r&z$TFViTForImageClassification.__init__?sw 3&3F3 ++!&EN ,,,,##.v/G/GH-   r2)rFrGr;rIc |j|||||||} | d} |j| dddddf} |dn|j|| } |s| f| ddz} | | f| zS| St| | | j| j S)a labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). rKrNr)labelsrr)lossrrr)r>r[hf_compute_lossr rr)r.rhrrrrcrr^rerr3rr_rs r1rkz TFViTForImageClassification.callMs4((%/!5%=# "!*1a(@A~t4+?+?vV\+?+]Y,F)-)9TGf$ Ev E)!//))   r2c|jryd|_t|ddMtj|jj 5|jj ddddt|ddetj|jj 5|jj dd|jjgdddyy#1swY|xYw#1swYyxYw)NTr>r[) r=r>r?r@r>r!rAr[r,r;rs r1rAz!TFViTForImageClassification.builds ::  4 % 1txx}}- %t$ % 4t , 8t334 M%%tT4;;3J3J&KL M M 9 % % M Ms C"%3C."C+.C7rl)NNNNNNNF)rhr8rr9rr:rr:rcr:rr:r^r9rer:rnz3Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]rm)rsrtrur&rrrMr_IMAGE_CLASS_CHECKPOINTr rO_IMAGE_CLASS_EXPECTED_OUTPUTrkrArwrxs@r1rYrY/s *+?@*.$4 1537,0/337&*04#(( -( 1( * ( - ( #1 ( $( .( !(  =( A( T Mr2rY)@rv __future__rcollections.abcrrTtypingrrrnumpynp tensorflowr?activations_tfrmodeling_tf_outputsr r r modeling_tf_utilsr r rrrrrtf_utilsrrutilsrrrrconfiguration_vitr get_loggerrsloggerrOrNrPrbrcr(Layerrr'rrrrrrr r#r=VIT_START_DOCSTRINGrMrBr(rYr$r2r1rts" ))/nn3uu(   H %:&8-Yell((Y|EM5<<--EMPWH++WHtHell((H<$.U\\''$.NH **H:N%,,$$N4@R##@RF3&5<<%%3&l[(U\\''[([(|%,%(T>c+%%+% +%\H%,,$$H: JM"68TJMJMr2