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mZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ G dd dZdS )zKeras layers API.    N)Layer)PreprocessingLayer)Input)
InputLayer)	InputSpec)ELU)	LeakyReLU)PReLU)ReLU)Softmax)ThresholdedReLU)AdditiveAttention)	Attention)MultiHeadAttention)Conv1D)Convolution1D)Conv1DTranspose)Convolution1DTranspose)Conv2D)Convolution2D)Conv2DTranspose)Convolution2DTranspose)Conv3D)Convolution3D)Conv3DTranspose)Convolution3DTranspose)DepthwiseConv1D)DepthwiseConv2D)SeparableConv1D)SeparableConvolution1D)SeparableConv2D)SeparableConvolution2D)
Activation)Dense)EinsumDense)	Embedding)Lambda)Masking)ClassMethod)InstanceMethod)InstanceProperty)SlicingOpLambda)
TFOpLambda)LocallyConnected1D)LocallyConnected2D)Add)add)Average)average)Concatenate)concatenate)Dot)dot)Maximum)maximum)Minimum)minimum)Multiply)multiply)Subtract)subtract)SyncBatchNormalization)LayerNormalization)UnitNormalization)CategoryEncoding)Discretization)HashedCrossing)Hashing)
CenterCrop)RandomBrightness)RandomContrast)
RandomCrop)
RandomFlip)RandomHeight)RandomRotation)RandomTranslation)RandomWidth)
RandomZoom)	Rescaling)Resizing)IntegerLookup)Normalization)StringLookup)TextVectorization)ActivityRegularization)AlphaDropout)Dropout)GaussianDropout)GaussianNoise)SpatialDropout1D)SpatialDropout2D)SpatialDropout3D)
Cropping1D)
Cropping2D)
Cropping3D)Flatten)Permute)RepeatVector)Reshape)UpSampling1D)UpSampling2D)UpSampling3D)ZeroPadding1D)ZeroPadding2D)ZeroPadding3D)tf2)BatchNormalization)RandomFourierFeatures)AveragePooling1D)	AvgPool1D)AveragePooling2D)	AvgPool2D)AveragePooling3D)	AvgPool3D)GlobalAveragePooling1D)GlobalAvgPool1D)GlobalAveragePooling2D)GlobalAvgPool2D)GlobalAveragePooling3D)GlobalAvgPool3D)GlobalMaxPool1D)GlobalMaxPooling1D)GlobalMaxPool2D)GlobalMaxPooling2D)GlobalMaxPool3D)GlobalMaxPooling3D)	MaxPool1D)MaxPooling1D)	MaxPool2D)MaxPooling2D)	MaxPool3D)MaxPooling3D)AbstractRNNCell)RNN)	SimpleRNN)SimpleRNNCell)StackedRNNCells)GRU)GRUCell)LSTM)LSTMCell)serialization)Wrapper)Bidirectional)DeviceWrapper)DropoutWrapper)ResidualWrapper)
ConvLSTM1D)
ConvLSTM2D)
ConvLSTM3D)CuDNNGRU)	CuDNNLSTM)TimeDistributed)deserialize)deserialize_from_json)get_builtin_layer)	serializec                       s    e Zd ZdZ fddZ  ZS )VersionAwareLayersa  Utility to be used internally to access layers in a V1/V2-aware fashion.

    When using layers within the Keras codebase, under the constraint that
    e.g. `layers.BatchNormalization` should be the `BatchNormalization` version
    corresponding to the current runtime (TF1 or TF2), do not simply access
    `layers.BatchNormalization` since it would ignore e.g. an early
    `compat.v2.disable_v2_behavior()` call. Instead, use an instance
    of `VersionAwareLayers` (which you can use just like the `layers` module).
    c                    s,   t   |t jjv r t jj| S t |S )N)r   populate_deserializable_objectsLOCALALL_OBJECTSsuper__getattr__)selfname	__class__ Q/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/__init__.pyr     s    zVersionAwareLayers.__getattr__)__name__
__module____qualname____doc__r   __classcell__r   r   r   r   r     s   
r   (  r   tensorflow.compat.v2compatv2tfZkeras.engine.base_layerr   Z%keras.engine.base_preprocessing_layerr   keras.engine.input_layerr   r   Zkeras.engine.input_specr   Zkeras.layers.activation.elur   Z"keras.layers.activation.leaky_relur   Zkeras.layers.activation.prelur	   Zkeras.layers.activation.relur
   Zkeras.layers.activation.softmaxr   Z(keras.layers.activation.thresholded_relur   Z)keras.layers.attention.additive_attentionr   Z keras.layers.attention.attentionr   Z+keras.layers.attention.multi_head_attentionr   Z!keras.layers.convolutional.conv1dr   r   Z+keras.layers.convolutional.conv1d_transposer   r   Z!keras.layers.convolutional.conv2dr   r   Z+keras.layers.convolutional.conv2d_transposer   r   Z!keras.layers.convolutional.conv3dr   r   Z+keras.layers.convolutional.conv3d_transposer   r   Z+keras.layers.convolutional.depthwise_conv1dr   Z+keras.layers.convolutional.depthwise_conv2dr   Z+keras.layers.convolutional.separable_conv1dr   r   Z+keras.layers.convolutional.separable_conv2dr    r!   Zkeras.layers.core.activationr"   Zkeras.layers.core.denser#   Zkeras.layers.core.einsum_denser$   Zkeras.layers.core.embeddingr%   Zkeras.layers.core.lambda_layerr&   Zkeras.layers.core.maskingr'   Zkeras.layers.core.tf_op_layerr(   r)   r*   r+   r,   Z2keras.layers.locally_connected.locally_connected1dr-   Z2keras.layers.locally_connected.locally_connected2dr.   Zkeras.layers.merging.addr/   r0   Zkeras.layers.merging.averager1   r2   Z keras.layers.merging.concatenater3   r4   Zkeras.layers.merging.dotr5   r6   Zkeras.layers.merging.maximumr7   r8   Zkeras.layers.merging.minimumr9   r:   Zkeras.layers.merging.multiplyr;   r<   Zkeras.layers.merging.subtractr=   r>   Z.keras.layers.normalization.batch_normalizationr?   Z.keras.layers.normalization.layer_normalizationr@   Z-keras.layers.normalization.unit_normalizationrA   Z,keras.layers.preprocessing.category_encodingrB   Z)keras.layers.preprocessing.discretizationrC   Z*keras.layers.preprocessing.hashed_crossingrD   Z"keras.layers.preprocessing.hashingrE   Z.keras.layers.preprocessing.image_preprocessingrF   rG   rH   rI   rJ   rK   rL   rM   rN   rO   rP   rQ   Z)keras.layers.preprocessing.integer_lookuprR   Z(keras.layers.preprocessing.normalizationrS   Z(keras.layers.preprocessing.string_lookuprT   Z-keras.layers.preprocessing.text_vectorizationrU   Z3keras.layers.regularization.activity_regularizationrV   Z)keras.layers.regularization.alpha_dropoutrW   Z#keras.layers.regularization.dropoutrX   Z,keras.layers.regularization.gaussian_dropoutrY   Z*keras.layers.regularization.gaussian_noiserZ   Z-keras.layers.regularization.spatial_dropout1dr[   Z-keras.layers.regularization.spatial_dropout2dr\   Z-keras.layers.regularization.spatial_dropout3dr]   Z!keras.layers.reshaping.cropping1dr^   Z!keras.layers.reshaping.cropping2dr_   Z!keras.layers.reshaping.cropping3dr`   Zkeras.layers.reshaping.flattenra   Zkeras.layers.reshaping.permuterb   Z$keras.layers.reshaping.repeat_vectorrc   Zkeras.layers.reshaping.reshaperd   Z$keras.layers.reshaping.up_sampling1dre   Z$keras.layers.reshaping.up_sampling2drf   Z$keras.layers.reshaping.up_sampling3drg   Z%keras.layers.reshaping.zero_padding1drh   Z%keras.layers.reshaping.zero_padding2dri   Z%keras.layers.reshaping.zero_padding3drj   tensorflow.pythonrk   __internal__enabledrl   Z1keras.layers.normalization.batch_normalization_v1ZBatchNormalizationV1ZBatchNormalizationV2Zkeras.layers.kernelizedrm   Z&keras.layers.pooling.average_pooling1drn   ro   Z&keras.layers.pooling.average_pooling2drp   rq   Z&keras.layers.pooling.average_pooling3drr   rs   Z-keras.layers.pooling.global_average_pooling1drt   ru   Z-keras.layers.pooling.global_average_pooling2drv   rw   Z-keras.layers.pooling.global_average_pooling3drx   ry   Z)keras.layers.pooling.global_max_pooling1drz   r{   Z)keras.layers.pooling.global_max_pooling2dr|   r}   Z)keras.layers.pooling.global_max_pooling3dr~   r   Z"keras.layers.pooling.max_pooling1dr   r   Z"keras.layers.pooling.max_pooling2dr   r   Z"keras.layers.pooling.max_pooling3dr   r   Z"keras.layers.rnn.abstract_rnn_cellr   Zkeras.layers.rnn.base_rnnr   Zkeras.layers.rnn.simple_rnnr   r   Z"keras.layers.rnn.stacked_rnn_cellsr   Zkeras.layers.rnn.grur   r   Zkeras.layers.rnn.gru_v1GRUV1	GRUCellV1Zkeras.layers.rnn.lstmr   r   Zkeras.layers.rnn.lstm_v1LSTMV1
LSTMCellV1GRUV2	GRUCellV2LSTMV2
LSTMCellV2keras.layersr   Zkeras.layers.rnn.base_wrapperr   Zkeras.layers.rnn.bidirectionalr   Zkeras.layers.rnn.cell_wrappersr   r   r   Zkeras.layers.rnn.conv_lstm1dr   Zkeras.layers.rnn.conv_lstm2dr   Zkeras.layers.rnn.conv_lstm3dr   Zkeras.layers.rnn.cudnn_grur   Zkeras.layers.rnn.cudnn_lstmr   Z!keras.layers.rnn.time_distributedr   Zkeras.layers.serializationr   r   r   r   r   r   r   r   r   <module>   st  