`i\dZddlmZmZddlZddlmZddlm Z m Z m Z ddl m Z mZmZmZmZddlmZdd lmZmZmZmZd d lmZejeZd Zd ZgdZ d Z!dZ"Gddej#j$Z%Gddej#j$Z&Gddej#j$Z'Gddej#j$Z(Gddej#j$Z)Gddej#j$Z*Gddej#j$Z+Gdde Z,d Z-d!Z.eGd"d#ej#j$Z/ed$e-Gd%d&e,Z0ed'e-Gd(d)e,eZ1gd*Z2dS)+zTensorFlow ResNet model.)OptionalUnionN)ACT2FN) TFBaseModelOutputWithNoAttention*TFBaseModelOutputWithPoolingAndNoAttention&TFImageClassifierOutputWithNoAttention)TFPreTrainedModelTFSequenceClassificationLosskeraskeras_serializable unpack_inputs) shape_list)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardlogging) ResNetConfigrzmicrosoft/resnet-50)rirz tiger catc eZdZ ddededededed d f fd Zd ejd ejfd Zdd ejde d ejfdZ ddZ xZ S)TFResNetConvLayerrrrelu in_channels out_channels kernel_sizestride activationreturnNc rtjd i||dz|_tj|||ddd|_tjddd |_| t|ntj d |_ ||_ ||_ dS) NvalidF convolution)rstridespaddinguse_biasnameh㈵>? normalizationepsilonmomentumr'linear)super__init__ pad_valuer layersConv2DconvBatchNormalizationr*r Activationrrr)selfrrrrrkwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/resnet/modeling_tf_resnet.pyr1zTFResNetConvLayer.__init__6s ""6"""$)L'' k67]biv(   #\< -PS -  - - - - - -dry * * * * * * * *r<rvc veZdZdZ ddedededed ed d f fd Zddejde d ejfdZ ddZ xZ S)TFResNetBottleNeckLayera% A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. rrrrrr reductionrNc tjd i|||kp|dk}||z}t||dd|_t|||d|_t||ddd|_|rt |||dn tj d d |_ t||_ dS) Nrrx)rr'rzryzlayer.2)rrr'r|r.r}r/) r0r1rconv0r~rrpr r3r7r|rr) r8rrrrrr9rreduces_channelsr:s r;r1z TFResNetBottleNeckLayer.__init__s ""6""" +| ; Jv{'94&{4DRSZcddd &'79IRX_hiii &'7STaeluvvv % D [,vJ W W W W(( (CC !,r<Fr=rCc|}|||}|||}|||}|||}||z }||}|SrE)rr~rr|rrs r;rGzTFResNetBottleNeckLayer.calls|zz,zBB zz,zBB zz,zBB ==H=== |44 r<c4|jrdSd|_t|ddPtj|jj5|jddddn #1swxYwYt|ddPtj|jj5|jddddn #1swxYwYt|ddPtj|jj5|jddddn #1swxYwYt|ddStj|j j5|j dddddS#1swxYwYdSdS)NTrr~rr|) rIrJr?rKrr'rLr~rr|rMs r;rLzTFResNetBottleNeckLayer.buildst :  F 4$ ' ' 3tz// ' '   &&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 4$ ' ' 3tz// ' '   &&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 4$ ' ' 3tz// ' '   &&& ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 4T * * 6t}122 * * ##D))) * * * * * * * * * * * * * * * * * * 7 6sHA''A+.A+!CC C D))D-0D-#F  FF)rrrrPrQrrZs@r;rrs  ---- -  -  - ------,dry********r<rc reZdZdZ ddedededededd f fd Zdd ejd e dejfdZ ddZ xZ S) TFResNetStagez4 A ResNet stage composed of stacked layers. r!r]rrrdepthrNc tjdi|jdkrtnt||jdg}|fdt |dz Dz }||_dS)N bottleneckzlayers.0)rrr'c Dg|]}jd|dzS)zlayers.rr{)rd).0ir]layerrs r; z*TFResNetStage.__init__..!sO    E, 9JQb[\_`[`QbQb c c c   r<rr/)r0r1 layer_typerrvrdrange stage_layers) r8r]rrrrr9r3rr:s ` ` @r;r1zTFResNetStage.__init__s ""6"""+1+< +L+L''Rd% \&VM^eopppq      519%%    #r<Fr=rCc4|jD]}|||}|SrE)r)r8r=rCrs r;rGzTFResNetStage.call's2& B BE 5AAALLr<c|jrdSd|_t|ddP|jD]J}tj|j5|ddddn #1swxYwYIdSdS)NTr)rIrJrr?rKr'rLr8rNrs r;rLzTFResNetStage.build,s :  F 4 . . :* & &]5:..&&KK%%%&&&&&&&&&&&&&&& ; : & &A&&A* -A* )r!r!rPrQ) rRrSrTrnrrUr1r?rWrXrGrLrYrZs@r;rrs hi # #" #14 #DG #QT #ad #  # # # # # #dry &&&&&&&&r<rc ^eZdZdeddffd Z d dejded ed edef d Z dd Z xZ S)TFResNetEncoderr]rNc tjdi|t||j|jd|jrdnd|jddg|_tt|j|jdd|jddD];\}\}}}|j t||||d|dzO P P6 6  v v 1A1 \5 K  }V[,V[bslmpqlqbsbsttt u u u u v vr<FTr=output_hidden_states return_dictrCc|rdnd}|jD]}|r||fz}|||}|r||fz}|std||fDSt||S)Nr/rFc3K|]}||V dSrQr/)rvs r; z'TFResNetEncoder.call..\s"SSqQ]]]]]SSr<)last_hidden_state hidden_states)rtupler)r8r=rrrCr stage_modules r;rGzTFResNetEncoder.callIs3<  K I IL# @ - ? '< xHHHLL  <)\O;M TSS\=$ASSSSS S/,^kllllr<c|jrdSd|_t|ddP|jD]J}tj|j5|ddddn #1swxYwYIdSdS)NTr)rIrJrr?rKr'rLrs r;rLzTFResNetEncoder.build`s :  F 44 ( ( 4 & &]5:..&&KK%%%&&&&&&&&&&&&&&& 5 4 & &r)FTFrQ) rRrSrTrr1r?rWrXrrGrLrYrZs@r;rr6sv|v$vvvvvv*&+  mmim#m m  m * mmmm.&&&&&&&&r<rc4eZdZdZeZdZdZedZ dS)TFResNetPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. resnetrgcbdtjd|jjddftjiS)Nrg)shapedtype)r? TensorSpecr]rbfloat32)r8s r;input_signaturez'TFResNetPreTrainedModel.input_signaturets0 T4;;SUXZ]4^fhfp q q qrrr<N) rRrSrTrnr config_classbase_model_prefixmain_input_namepropertyrr/r<r;rrjsN  L $O ssXsssr<rad This model is a TensorFlow [keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior. Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. a> Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. ceZdZeZdeddffd Ze d dejde e de e d e de e eje ff d Zd d ZxZS)TFResNetMainLayerr]rNc tjdi|||_t|d|_t |d|_tj d|_ dS)Nr_r}encoderT)keepdimsr/) r0r1r]r\r_rrr r3GlobalAveragePooling2Dr`rfs r;r1zTFResNetMainLayer.__init__sm""6""" *6 CCC &vI>>> l9949HH r<FrgrrrCc||n |jj}||n |jj}tj|gd}|||}|||||}|d}||}tj|d}tj|d}d} |ddD]} | td | Dz} |s||f| zS|r| nd} t||| S) N)rr!rr)permrFrrrCrrrrr!r/rc3@K|]}tj|dVdS)rN)r? transpose)rhs r;rz)TFResNetMainLayer.call..s/1f1fTU",q,2O2O1f1f1f1f1f1fr<)r pooler_outputr) r]ruse_return_dictr?rr_rr`rr) r8rgrrrCembedding_outputencoder_outputsr pooled_outputrr=s r;rGzTFResNetMainLayer.callsT%9$D $+Jj &1%$. 04&* i'tnd^    uRY!KK L ]DC(((((((((r<rz ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. cFeZdZdeddffd ZdejdejfdZee e e e e ee dd eejd eejd eed eed edeeeje ff dZddZxZS)TFResNetForImageClassificationr]rNc <tj|fi||j|_t|d|_|jdkr&t j|jdn t jdd|_ ||_ dS)Nrr}rz classifier.1r.) r0r1 num_labelsrrr r3Denser7classifier_layerr]rfs r;r1z'TFResNetForImageClassification.__init__s**6*** +'X>>>  1$$ L  v0~  F F F(((GG   r<rrc|tj|}||}|SrQ)r r3Flattenr)r8rrlogitss r; classifierz)TFResNetForImageClassification.classifiers5 L " "1 % %&&q)) r<)rrrrFrglabelsrrrCc6||n |jj}|||||}|r|jn|d}||}|dn|||} |s|f|ddz} | | f| zn| St | ||jS)a) labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. 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