|j2rddlmZddlmZmZmZmZddlZddlmZddl m Z m Z ddl m Z ddlmZdd lmZmZmZerdd lmZgZed Zed'dZed(dZdZed)dZed*dZdZe d+d,dZed-dZed.dZ d+d Z d+d/d&ZdS)0) annotations) TYPE_CHECKINGAnyTypeVaroverloadN)_typing) check_typecheck_variable_and_dtype) in_pir_mode)Variable) LayerHelpercorein_dynamic_mode)SequenceTarray list[Any]returnintcdSNrs c/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/tensor/array.py array_lengthr#s+.3 paddle.TensorcdSrrrs rrr's9<rctr.t|ts Jdt|St rat|t jjr|stdt j |St|tr|j tjjjkrtdt%d it'}|d}d|_|dd|gid |gi |S) a6 This OP is used to get the length of the input array. Args: array (list|Tensor): The input array that will be used to compute the length. In dynamic mode, ``array`` is a Python list. But in static graph mode, array is a Tensor whose VarType is DENSE_TENSOR_ARRAY. Returns: Tensor, 0-D Tensor with shape [], which is the length of array. Examples: .. code-block:: python >>> import paddle >>> arr = paddle.tensor.create_array(dtype='float32') >>> x = paddle.full(shape=[3, 3], fill_value=5, dtype="float32") >>> i = paddle.zeros(shape=[1], dtype="int32") >>> arr = paddle.tensor.array_write(x, i, array=arr) >>> arr_len = paddle.tensor.array_length(arr) >>> print(arr_len) 1 9The 'array' in array_write must be a list in dygraph mode8array should be tensor array variable in array_length Oprint64dtypeTlod_array_lengthXOuttypeinputsoutputsN)r)r isinstancelistlenr paddlepirValueis_dense_tensor_array_type TypeError_pir_opsrr r*rVarDescVarTypeDENSE_TENSOR_ARRAYrlocals"create_variable_for_type_inference stop_gradient append_op)rhelpertmps rrr+s[2%&&  G  &5zz 5&*"233 3355 J ++E2225(++ zT\1DDDJ 88vxx8877g7FF #%>SEN     rlist[T]icdSrrrr@s r array_readrCfs7:srcdSrrrBs rrCrCjsILrc6trqt|ts Jdt|ts Jd|jdgks Jd|d}||St rbt|tjj r| stdtj ||St|ddgd tdit!}t|tr|jt$jjjkrtd ||j }|d |g|gd d|gi|S)a This OP is used to read data at the specified position from the input array. Case: .. code-block:: text Input: The shape of first three tensors are [1], and that of the last one is [1,2]: array = ([0.6], [0.1], [0.3], [0.4, 0.2]) And: i = [3] Output: output = [0.4, 0.2] Args: array (list|Tensor): The input array. In dynamic mode, ``array`` is a Python list. But in static graph mode, array is a Tensor whose ``VarType`` is ``DENSE_TENSOR_ARRAY``. i (Tensor): 1-D Tensor, whose shape is [1] and dtype is int64. It represents the specified read position of ``array``. Returns: Tensor, A Tensor that is read at the specified position of ``array``. Examples: .. code-block:: python >>> import paddle >>> arr = paddle.tensor.create_array(dtype="float32") >>> x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32") >>> i = paddle.zeros(shape=[1], dtype="int32") >>> arr = paddle.tensor.array_write(x, i, array=arr) >>> item = paddle.tensor.array_read(arr, i) >>> print(item.numpy()) [[5. 5. 5.]] z6The 'array' in array_read must be list in dygraph modez>> import paddle >>> arr = paddle.tensor.create_array(dtype="float32") >>> x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32") >>> i = paddle.zeros(shape=[1], dtype="int32") >>> arr = paddle.tensor.array_write(x, i, array=arr) >>> item = paddle.tensor.array_read(arr, i) >>> print(item.numpy()) [[5. 5. 5.]] zBThe input data 'x' in array_write must be Variable in dygraph modez=The index 'i' in array_write must be Variable in dygraph moderFrGrNr!zNThe index 'i' should not be greater than the length of 'array' in dygraph moder@r#rTz$x should be pir.Value, but received .rHrOz7array should be tensor array variable in array_write Op.outnamer*r%write_to_arrayrJr(r))rT)#rr-r rLrM create_arrayr%r.r/appendr r r0r1r2r4r*r3r5base libpaddleDataType UNDEFINEDcast array_write_r rr9rr6r7r8create_variabler\r<)rOr@rr=s rrTrTs$H@!X&&  P  &!X&&  K  &w1#~~~ B~~ FF1II = ))E%&&  G  &CJJ \ s5zz>>E!HH LLOOO ( C'MBBB!VZ-.. OM477MMMNN N  ufj&677 I7799 I GHHH =O0099E ;&+/8B B B Au{++A$$UAq111  C'MBBB1cH}55577fhh77  uh// :!5!HHHM =** )))\)<g+E !A3''UG$     rr%_typing.DTypeLikeinitialized_listSequence[paddle.Tensor] | None#paddle.Tensor | list[paddle.Tensor]cg}|Jt|ttfstdt |t|}|D]H}t|t t jjfs tdt |dItr|Strt|tj j tjfs$t jj|}t j|}|D]d}|t jjjjkrt j||}t j||t1|e|St3d it5}||jdtj j j|}|D]!}t=|t1||"|S) aT This OP creates an array. It is used as the input of :ref:`api_paddle_tensor_array_array_read` and :ref:`api_paddle_tensor_array_array_write`. Args: dtype (str): The data type of the elements in the array. Support data type: float32, float64, int32, int64 and bool. initialized_list(list): Used to initialize as default value for created array. All values in initialized list should be a Tensor. Returns: list|Tensor, An empty array. In dynamic mode, ``array`` is a Python list. But in static graph mode, array is a Tensor whose ``VarType`` is ``DENSE_TENSOR_ARRAY``. Examples: .. code-block:: python >>> import paddle >>> arr = paddle.tensor.create_array(dtype="float32") >>> x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32") >>> i = paddle.zeros(shape=[1], dtype="int32") >>> arr = paddle.tensor.array_write(x, i, array=arr) >>> item = paddle.tensor.array_read(arr, i) >>> print(item.numpy()) [[5. 5. 5.]] NzBRequire type(initialized_list) should be list/tuple, but received zOAll values in `initialized_list` should be Variable or pir.Value, but received rYrrZr[rSr)r-r.tupler4r*r r0r1r2rr rr6r7rbr` frameworkconvert_np_dtype_to_dtype_r5r^rarcrdrerrr9rfr\r8rT)r%rhrvalrNr= tensor_arrays rr^r^5sB E#*T5M:: mUYZjUkUkmm %&&#&**:;<< nbfgjbkbknnn    %$,"6 !FGG LK)DDUKKEo**511 F FC -6@@@k#u-- O ( (c<3D3D E E E E 1111&,&<&<K%%%%8'=' '  Q QC #l!;!;< P P P P Pr)rrrr)rrrr)rr?r@rrr)rrr@rrrr)rOrr@rrrPrrQ)rOrr@rrrUrrU)rOrr@rrrrr)r%rgrhrirrj) __future__rtypingrrrrr0rbase.data_feederr r base.frameworkr common_ops_importr rmrrrcollections.abcr__all__rrrCrTr^rrrrxs #"""""888888888888 CCCCCCCC((((((((((((::::::::::)((((((  GCLL ... . <<< <888v ::: : LLL LKKK\ 6:$$$$ $       ddddR8<FFFFFFFr