0h!fdZddlZddlmcmZddlmZddl m Z e dGddeZ y)zContains the Reshape layer.N)Layer) keras_exportzkeras.layers.Reshapec>eZdZdZfdZdZdZdZfdZxZ S)ReshapeaLayer that reshapes inputs into the given shape. Input shape: Arbitrary, although all dimensions in the input shape must be known/fixed. Use the keyword argument `input_shape` (tuple of integers, does not include the samples/batch size axis) when using this layer as the first layer in a model. Output shape: `(batch_size,) + target_shape` Example: >>> # as first layer in a Sequential model >>> model = tf.keras.Sequential() >>> model.add(tf.keras.layers.Reshape((3, 4), input_shape=(12,))) >>> # model.output_shape == (None, 3, 4), `None` is the batch size. >>> model.output_shape (None, 3, 4) >>> # as intermediate layer in a Sequential model >>> model.add(tf.keras.layers.Reshape((6, 2))) >>> model.output_shape (None, 6, 2) >>> # also supports shape inference using `-1` as dimension >>> model.add(tf.keras.layers.Reshape((-1, 2, 2))) >>> model.output_shape (None, 3, 2, 2) c Dt|di|t||_y)aCreates a `tf.keras.layers.Reshape` layer instance. Args: target_shape: Target shape. Tuple of integers, does not include the samples dimension (batch size). **kwargs: Any additional layer keyword arguments. N)super__init__tuple target_shape)selfr kwargs __class__s _/var/www/html/engine/venv/lib/python3.12/site-packages/tf_keras/src/layers/reshaping/reshape.pyr zReshape.__init__<s" "6"!,/cJt|}dj||}d\}}t|D]#\}}|dkr||}td|d||z}%t j |t }|"|dk(s||zdk7r t|||z||<|S||k7r t||S)aFind and replace a missing dimension in an output shape. This is a near direct port of the internal Numpy function `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c` Args: input_shape: Shape of array being reshaped output_shape: Desired shape of the array with at most a single -1 which indicates a dimension that should be derived from the input shape. Returns: The new output shape with a -1 replaced with its computed value. Raises: ValueError: If the total array size of the output_shape is different than the input_shape, or more than one unknown dimension is specified. zNtotal size of new array must be unchanged, input_shape = {}, output_shape = {})NrzTThere must be at most one unknown dimension in output_shape. Received: output_shape=.)dtype)listformat enumerate ValueErrornpprodint) r input_shape output_shapemsgknownunknownindexdimoriginals r_fix_unknown_dimensionzReshape._fix_unknown_dimensionGs(L)  228&\3 !w#L1 JE3Qw?#G$@@L~QP   77;c2  zX-2 o%$,$5L ! S/ !rctj|j}d|ddvr&|dg}|td|jDz }n(|dg}||j |dd|jz }tj|S)Nrrc3.K|] }|dk7r|ndyw)Nr).0ss r z/Reshape.compute_output_shape..~s "+,Q"W$&"s)tf TensorShapeas_listr r r%)r rrs rcompute_output_shapezReshape.compute_output_shapeysnn[199; ;qr? "'N+L E"040A0A" L(N+L D77AB!2!2 L~~l++rctj|tj|df|jz}tjs*|j |j |j|S)Nr)r,reshapeshaper executing_eagerly set_shaper/)r inputsresults rcallz Reshape.calls^FRXXf%5a%8$:T=N=N$NO##%   T66v||D E rcd|ji}t| }tt |j t |j zS)Nr )r r get_configdictritems)r config base_configrs rr9zReshape.get_configsI $"3"34g(* D**,-V\\^0DDEEr) __name__ __module__ __qualname____doc__r r%r/r7r9 __classcell__)rs@rrrs)> 00d ,FFrr) rAnumpyrtensorflow.compat.v2compatv2r,tf_keras.src.engine.base_layerr tensorflow.python.util.tf_exportrrrrrrIsD"!!0:$%xFexF&xFr