AVh,pdZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZeZd Zej&ej(ej*ej,ej.fZd Zd Zed ge j6ddZdZej<j?dey)z"Tensor __getitem__ override logic.N)dtypes)ops)tensor) tensor_shape) gen_math_ops)dispatch) tf_exportzyOnly integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indicescVt|tjxr|jduSN) isinstancer Dimensionvalue)ds ]/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/tensor_getitem_override.py_is_undefined_dimensionr-s" A|-- . B177d?Bc:t|tjtjfryt |dd}|?t j|tvs$|jr:t|jdk(r!ttdj|zyy)z6Check if a given value is a valid index into a tensor.Ndtypez , got {!r})r numbersIntegralrr getattrras_dtype_SUPPORTED_SLICE_DTYPESshapelen TypeError_SLICE_TYPE_ERRORformat)idxrs r _check_indexr!1sg&& (>(>?@ #w %% mvu-5LL iiC Na' % (;(;C(@@ AA(irz__operators__.getitem)v1cddlm}ddlm}t j |}|!tj r|j|St|tsdt|tjr|jtjk(s-t|tjr&|jtk(r|j!||St|t"t$fs|g}ggg}}}d}d\} } d\} } d} |D]}t|t&rd}|j(|j*|j,fD]}|t|tjs |jtj.k(rtj.}N|jtj0k(r$|tj.k7rtj0}|jtj2k(s|tj2}|j(Ft5|j(s1t7|j(|j9|j(n>|#|j9|j;d|n|j9d| d|zz} |j*Ft5|j*s1t7|j*|j9|j*n>|#|j9|j;d|n|j9d| d|zz} |j,Gt5|j,s2t7|j,|j9|j,n|$|j9|j;d|nl|j9dnY|t<ur=|j9d|j9d|j9d| d|zz} n||j>ur<|j9d|j9d|j9d| d|zz} nt7||j9||j9|dzt|tjrg|jtj2k(s|jtj.k(r-|j9|j;d|jn|j9d| d|zz} |dz }t j@dd |g|z|z|zd 5}|rsdd lm!}|jE||jE||jE|}}}|jtj.k(s:|jtj.k(s|jtj.k(r|jtj.k7r$tGjH|tj.}|jtj.k7r$tGjH|tj.}|jtj.k7rjtGjH|tj.}nD|jtj2k(r&|jtj2k(r|jtj2k(r|jtj2k7r$tGjH|tj2}|jtj2k7r$tGjH|tj2}|jtj2k7rLtGjH|tj2}n'|j;gtj0}|x}x}}|jK||||| | | | | || cdddS#1swYyxYw)am Overload for Tensor.__getitem__. This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that currently only support basic indexing. That means that using a non-scalar tensor as input is not currently allowed. Some useful examples: ```python # Strip leading and trailing 2 elements foo = tf.constant([1,2,3,4,5,6]) print(foo[2:-2]) # => [3,4] # Skip every other row and reverse the order of the columns foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]]) print(foo[::2,::-1]) # => [[3,2,1], [9,8,7]] # Use scalar tensors as indices on both dimensions print(foo[tf.constant(0), tf.constant(2)]) # => 3 # Insert another dimension foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]]) print(foo[tf.newaxis, :, :]) # => [[[1,2,3], [4,5,6], [7,8,9]]] print(foo[:, tf.newaxis, :]) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]] print(foo[:, :, tf.newaxis]) # => [[[1],[2],[3]], [[4],[5],[6]], [[7],[8],[9]]] # Ellipses (3 equivalent operations) foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]]) print(foo[tf.newaxis, :, :]) # => [[[1,2,3], [4,5,6], [7,8,9]]] print(foo[tf.newaxis, ...]) # => [[[1,2,3], [4,5,6], [7,8,9]]] print(foo[tf.newaxis]) # => [[[1,2,3], [4,5,6], [7,8,9]]] # Masks foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]]) print(foo[foo > 2]) # => [3, 4, 5, 6, 7, 8, 9] ``` Notes: - `tf.newaxis` is `None` as in NumPy. - An implicit ellipsis is placed at the end of the `slice_spec` - NumPy advanced indexing is currently not supported. Purpose in the API: This method is exposed in TensorFlow's API so that library developers can register dispatching for `Tensor.__getitem__` to allow it to handle custom composite tensors & other custom objects. The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation. Args: tensor: An tensor.Tensor object. slice_spec: The arguments to Tensor.__getitem__. var: In the case of variable slice assignment, the Variable object to slice (i.e. tensor is the read-only view of this variable). Returns: The appropriate slice of "tensor", based on "slice_spec". Raises: ValueError: If a slice range is negative size. TypeError: If the slice indices aren't int, slice, ellipsis, tf.newaxis or scalar int32/int64 tensors. r) constant_op) array_opsN)rmask)rr)rr strided_sliceF) skip_on_eager)array_ops_stack) begin_maskend_maskshrink_axis_mask new_axis_mask ellipsis_maskvarname)&tensorflow.python.frameworkr$tensorflow.python.opsr%rconvert_to_tensor_numpy_style_slicing_numpy_style_getitemr bool tensor_libTensorrrnpndarray boolean_masklisttuple _BaseSlicestartstopstepint64int32int16rr!appendconstantEllipsisnewaxis name_scoper)stackrcastr')r slice_specr/r$r%beginendstridesindexr-r,r*r+r.srtr0r) packed_begin packed_endpacked_strides var_emptys r _slice_helperrWAs L6-   (&[S--  & &z 22T" Z!2!2 3&++- Z ,$&  ! !j ! AA Ju .JBg% %$(!-!*h- >a!Z e(! 9Jq**;*;<  77fll ",,% WW $&,,)>,,% WW $,,%  % ..  h ll1o jjm nnQU #m i  ll1o jjm nnQU #m1o ll1o jjQ Q )) *ww&,,&!''V\\*A{++AQWW+=>q1:& QJE}>B ~~ x%'#-7,# 7    &    $    (!/Jl    ,   fll *   &,, .    -%**<F,   v|| +#((V\\B*   6<< /',,^V\\J.   &,, .    ,  FLL 0    -%**<F,   v|| +#((V\\B*   6<< /',,^V\\J.&&r&>i3< [[1,2], [4,5]] A[:2,:2].assign(22. * tf.ones((2, 2)))) print(A) # => [[22, 22, 3], [22, 22, 6], [7,8,9]] ``` Note that assignments currently do not support NumPy broadcasting semantics. Args: var: An `ops.Variable` object. slice_spec: The arguments to `Tensor.__getitem__`. Returns: The appropriate slice of "tensor", based on "slice_spec". As an operator. The operator also has a `assign()` method that can be used to generate an assignment operator. Raises: ValueError: If a slice range is negative size. TypeError: TypeError: If the slice indices aren't int, slice, ellipsis, tf.newaxis or int32/int64 tensors. )rWr)r/rLs r_slice_helper_varrYsR syy{J 44r __getitem__r ) __doc__rnumpyr9r1rrrr7rr2rtensorflow.python.utilr tensorflow.python.util.tf_exportr slicer>rrDrC int32_refrB int64_refrrr!add_dispatch_supportrWrYr8_override_operatorrrres ).+<4.+6   "<<v7G7G!<<)9)9;C B  "r* H+HV)5X $$]MBr