&Vf5ddlZddlZddlmZddlmZddlmZddlm Z dZ dZ dd Z dd Z d ZdZdZddZeddZdS)N)tree) keras_exportcanonicalize_axis)to_tuple_or_listc t|}t|}|}|}t|t|kr&dgt|t|z z|z}t|t|kr&dgt|t|z z|z}t|}tt|D]}||dkr ||||<||||dkrdn||||<<||dks||||||kr ||||<ntd||d||d|d|d |S)aSBroadcast input shapes to a unified shape. Convert to list for mutability. Args: shape1: A tuple or list of integers. shape2: A tuple or list of integers. Returns: output_shape (list of integers or `None`): The broadcasted shape. Example: >>> broadcast_shapes((5, 3), (1, 3)) [5, 3] Nz2Cannot broadcast shape, the failure dim has value z!, which cannot be broadcasted to z. Input shapes are: z and .)listlenrange ValueError)shape1shape2 origin_shape1 origin_shape2 output_shapeis Z/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/ops/operation_utils.pybroadcast_shapesr s &\\F &\\FMM 6{{S[[  F c&kk12V; 6{{S[[  F c&kk12V;<>$QiLOO AY &,Qi1nndd&)LOOayA~~!2fQi6!96L6L"() Q NayNNCI!9NN)6NN=JNNN c4t|}t|ttt|zfdDt|fdt D}t |S)aCompute the output shape for the `expand_dims` operation. Args: input_shape: Input shape. axis: int or sequence of ints for the axis to expand. Returns: Tuple of ints: The output shape after the `expand_dims` operation. Nc0g|]}t|Sr).0aout_ndims r z4compute_expand_dims_output_shape..Fs$ 9 9 9q a * * 9 9 9rc:g|]}|vrdntSr )next)raxaxis shape_iters rrz4compute_expand_dims_output_shape..Hs:24R4ZZT*--r)r r riterr tuple) input_shaper# new_shaperr$s ` @@r compute_expand_dims_output_shaper)7s{##K |; D ! !D4yy3{+++H 9 9 9 9D 9 9 9Dk""J8=hI   rvalid channels_lastc$||n|}t|}tj|}|dkr |dd}n |dd}g}tt |D]$}||d||<||%tj|}|dkrctj||z |z dz} tt | D](}||vr"| |dkrtd|d |d )n6|d krtj|dz |z dz} ntd |d | D} |D]}d| |<t| } |dkr|df| z|dfz} n|d|df| z} | S)a3Computes the output shape of pooling operations. Args: input_shape: Input shape. Must be a tuple of integers. pool_size: Size of the pooling operation. Must be a tuple of integers. strides: Stride of the pooling operation. Must be a tuple of integers. Defaults to `pool_size`. padding: Padding method. Available methods are `"valid"` or `"same"`. Defaults to `"valid"`. data_format: String, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, weight)`. Defaults to `"channels_last"`. Returns: Tuple of ints: The output shape of the pooling operation. Examples: # Basic usage with square pooling on a single image >>> compute_pooling_output_shape((1, 4, 4, 1), (2, 2)) (1, 2, 2, 1) # Strided pooling on a single image with strides different from pool_size >>> compute_pooling_output_shape((1, 4, 4, 1), (2, 2), strides=(1, 1)) (1, 3, 3, 1) # Pooling on a batch of images >>> compute_pooling_output_shape((32, 4, 4, 3), (2, 2)) (32, 2, 2, 3) Nr+r r*rz@Computed output size would be negative. Received: `inputs.shape=z` and `pool_size=`.samezGArgument `padding` must be either 'valid' or 'same'. Received: padding=c,g|]}t|Srintrrs rrz0compute_pooling_output_shape..AAAqCFFAAAr) r nparrayr r appendfloorrr&) r' pool_sizestridespadding data_formatinput_shape_origin spatial_shape none_dimsroutput_spatial_shapers rcompute_pooling_output_shaperBNsXN#?iiGk**(;''Ko%%#AbD) #ABB I 3}%% & &     # "M!    Q   ##I' Hmi/7: ; ;a ? s/0011  A !!&:1&=&A&A Q%0QQCLQQQ  F  !x):g(EFFJ ! ! !   BA,@AAA ''"&Q !566o%%  " $" #!"%' (   q ! q !  !!  rr c |dkr|dd}||d|fz}n|dd}||d|fz}t|t|krtd|d|dt|tr|ft|z}t|tr|ft|z}t|t|krtd |d |dg} t j|}t t|D]$} || d|| <| | %t j|dd } t j|}|d krot j||| dz zz dz |z dz} t t| D]+} | | vr%| | d krtd|d|d|d,n=|dks|dkrt j|dz |z dz} ntd|dd| D} | D]} d| | <t| } |dkr|d f| z|dfz} n|d |df| z} | S)z%Compute the output shape of conv ops.r+r r-r.NzNKernel shape must have the same length as input, but received kernel of shape z and input of shape r zlDilation must be None, scalar or tuple/list of length of inputs' spatial shape, but received `dilation_rate=z` and input of shape r*rz?Computed output size would be negative. Received `inputs shape=z`, `kernel shape=z`, `dilation_rate=r/r0causalz9`padding` must be either `'valid'` or `'same'`. Received c,g|]}t|Srr2r4s rrz-compute_conv_output_shape..r5r) r r isinstancer3r6r7r r8r9r&)r'filters kernel_sizer;r<r= dilation_rater? kernel_shaper@rkernel_spatial_shaperArs rcompute_conv_output_shaperMso%%#AbD) "k"ow%?? #ABB "k!ng%>>  <C ,,,, -+ - -) - - -   -%%>&(3}+=+== '32*s=111 =S//// -+ - -* - - -   IH]++M 3}%% & &     # "M!    Q   8L"$566H]++M' H2F2J!KKaO     s/0011  A !!&:1&=&A&A 8%088%188'4888  F  g11!x):g(EFFJ       BA,@AAA ''"&Q !566o%% ^  4 4 R8H7J J  $A R(89>MVBZ0 #44L 6{{a   6{{a     rc *|d}|dkrtd|d|d|dd|vrtd|DStj|}|d kr2|tj|krtd |d |d||Sd}d}t |D]\}}|dkr|}||z}|d ks ||zd krtd |d |d |d|t |} ||z| |<t| S)zConverts `-1` in `newshape` to either an actual dimension or `None`. This utility does not special case the 0th dimension (batch size). r-r z4There must be at most one unknown dimension (-1) in z . Received: =r Nc3*K|]}|dkr|ndVdS)r-Nr)rdims r z/compute_reshape_output_shape..(s.DDCC2IISS4DDDDDDrrzFThe total size of the tensor must be unchanged. Received: input_shape=z, zyThe total size of the tensor must be unchanged, however, the input size cannot by divided by the specified dimensions in z. Received: input_shape=)countrr&mathprod enumerater ) r'newshapenewshape_arg_nameunknown_dim_count input_sizeknown_output_sizeunknown_dim_indexindexrUrs rcompute_reshape_output_shaperbs !r**1 N  N N.? N NBJ N N N    {DD8DDDDDD;''JA 8,, , ,M*MM.?MMBJMM ))%% s "99 %    $  A.?!?1!D!D .  . .:E . .! . .$, . .   >>L&04E&EL"#   rc,t|tdddSt|tkr0tdtdt|dtfd|DS)a Compute the output shape for the `transpose` operation. Args: input_shape: Input shape. axes: Permutation of the dimensions for the `transpose` operation. Returns: Tuple of ints: The output shape after the `transpose` operation. Nr-zDaxis must be a list of the same length as the input shape, expected z, but received r c3(K|] }|V dSNr)rr"r's rrVz1compute_transpose_output_shape..]s'00RR000000r)r r&r r)r'axess` rcompute_transpose_output_shapergJs{##K |[2&''' 4yyC $$$$ FK(( F F9.ds+++!+++rr T)reverse)r r&sorted)shaper#keepdimsr"s r reduce_shapero`s KKE |  ++U+++,, ,99   BE"IIU||t,,,  Bb U||rzkeras.utils.get_source_inputscTt|ds|S|j\}}}|r|js|gS|j|}|jrt j|jSg}|jD]F}t|}|D]2tfd|Dr| 3G|S)zReturns the list of input tensors necessary to compute `tensor`. Output will always be a list of tensors (potentially with 1 element). Args: tensor: The tensor to start from. Returns: List of input tensors. _keras_historyc3 K|]}|uV dSrer)rtxs rrVz$get_source_inputs..s'>>!1A:>>>>>>r) hasattrrq_inbound_nodesis_inputrflattenoutput_tensors input_tensorsget_source_inputsallr8)tensor operation node_indexrjnodesource_tensorsprevious_sourcesrts @rr{r{rs 6+ , , %4Iz1 "I4"x' 3 = "< 344 4N, 1 1#4V#<#< )11A>>>>~>>>>>1&--a0001" !r)r*r+)r r*r+r )NF)rXnumpyr6 keras.srcrkeras.src.api_exportr&keras.src.backend.common.backend_utilsrrrr)rBrMrQrbrgror{rrrrs= ------DDDDDDCCCCCC)))X6  VVVVz  MMMM`   F---`111,$-..""/."""r