ёi2SSKJr SSKJrJrJrJr SSKrSSKJr SSK J r J r SSK J r SSKJr SS KJrJrJr \(aSS KJr /r\"S 5r\SS j5r\SS j5rSr\SSj5r\SSj5rSr\SSSjj5r\SSj5r\SSj5rSSjrSSSjjrg) ) annotations) TYPE_CHECKINGAnyTypeVaroverloadN)_typing) check_typecheck_variable_and_dtype) in_pir_mode)Variable) LayerHelpercorein_dynamic_mode)SequenceTcgNarrays S/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/tensor/array.py array_lengthr#s+.cgrrrs rrr's9>> 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 Opint64dtypeTlod_array_lengthXOuttypeinputsoutputs)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 &5z 5&**"2"2333355J ++E225(++zzT\\11DDDJ 8vx877g7F #%>SEN   rcgrrris r array_readr>fs7:rcgrrr<s rr>r>jsILrc([5(ae[U[5(dS5e[U[5(dS5eURS/:XdS5eUR S5nX$[ 5(ah[U[RR5(aUR5(d [S5e[RRX5$[USS/S 5 [S0[!5D6n[U[5(a2UR"[$R&R(R*:wa [S 5eUR-UR.S 9nUR1S U/U/S .SU/0S9 U$)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%array should be tensor array variabler read_from_arrayr#Ir$r%)r>)rr)r*r shapeitemr r,r-r.r/r0r1r>r rr5r&rr2r3r4r6r!r8)rr=r9outs rr>r>nsoP%&& D &!X&& J &ww1#~ B ~ FF1Ix 5&**"2"2333355J ))%33 C'LA6VX65(++zzT\\11DDDCD D77ekk7J"s+SEN   rcgrrxr=rs r array_writerMs!$rcgrrrKs rrMrMsrcgrrrKs rrMrMsrcT[5(a[U[5(dS5e[U[5(dS5eURS/:XdS5eUR S5nUc[ UR 5n[U[5(dS5eU[U5::dS5eU[U5:aXU'U$URU5 U$[5(GaF[USS /S 5 [U[RR5(d[S [!U5S 35eUbI[U[RR5(aUR#5(d [S 5eUc)[R$R UR 5nUR [R&R(R*R,:wa [R."XR 5n[R$R1X U5 U$[USS /S 5 [3US[S 5 [5S0[75D6nUbR[U[5(a2UR [8R:R<R>:wa [S5eUcJURAURBS3[8R:R<R>UR S9nURESU/U/S.SU/0S9 U$)a This OP writes the input ``x`` into the i-th position of the ``array`` returns the modified array. If ``array`` is none, a new array will be created and returned. Args: x (Tensor): The input data to be written into array. It's multi-dimensional Tensor. Data type: float32, float64, int32, int64 and bool. i (Tensor): 0-D Tensor with shape [], which represents the position into which ``x`` is written. array (list|Tensor, optional): The array into which ``x`` is written. The default value is None, when a new array will be created and returned as a result. In dynamic mode, ``array`` is a Python list. But in static graph mode, array is a Tensor whose ``VarType`` is ``DENSE_TENSOR_ARRAY``. Returns: list|Tensor, The input ``array`` after ``x`` is written into. 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.]] zBThe input data 'x' in array_write must be Variable in dygraph modez=The index 'i' in array_write must be Variable in dygraph moderArBrrzNThe index 'i' should not be greater than the length of 'array' in dygraph moder=rrMz$x should be pir.Value, but received .rCrLz7array should be tensor array variable in array_write Op.outnamer&r!write_to_arrayrEr$r%)rM)#rr)r rGrH create_arrayr!r*r+appendr r r,r-r.r0r&r/r1base libpaddleDataType UNDEFINEDcast array_write_r rr5rr2r3r4create_variablerTr8)rLr=rr9s rrMrMsH!X&& P &!X&& K &ww1#~ B ~ FF1I = )E%&& G &CJ \  s5z>!H  LLO  C'MB!VZZ--..B47)1MN N  ufjj&6&6777799 GHH =OO009E ;;&++//88BB B A{{+A$$Uq1  C'MB1cH}57fh7  uh//::!5!5!H!HHM =** }D)\\))<<gg+E !A3'UG$   rcD/nUb=[U[[45(d[S[ U535e[U5nUHJn[U[ [ RR45(aM4[S[ U5S35e [5(aU$[5(a[U[RR[R45(d)[ RR R#U5n[ R$R'U5nUHtnU[ RR(RR*:wa[ R,"X05n[ R$R/XC[1U55 Mv U$[3S0[55D6nUR7UR8S3[RRR:US9nUHn[=U[1U5US9 M U$)a 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.]] zBRequire type(initialized_list) should be list/tuple, but received zOAll values in `initialized_list` should be Variable or pir.Value, but received rQrRrSrKr)r)r*tupler0r&r r,r-r.rr rr2r3rZrX frameworkconvert_np_dtype_to_dtype_r1rVrYr[r\r]rrr5r^rTr4rM)r!initialized_listrvalrIr9 tensor_arrays rrVrV5sB E#*T5M::TUYZjUkTlm %&#&***:*:;<<abfgjbkallmn   %$,,"6"6 !FGGKK))DDUKEoo**51C --66@@@kk#- OO ( (<3D E 11&,&<&<KK=%%%88'=' C #l!;< Pr)rz list[Any]returnint)r paddle.Tensorrfrh)rzlist[T]r=rhrfr)rrhr=rhrfrhr)rLrhr=rhrNonerfzlist[Any] | paddle.Tensor)rLrhr=rhrlist[paddle.Tensor]rfrj)rLrhr=rhrrhrfrh)r!z_typing.DTypeLikerczSequence[paddle.Tensor] | Nonerfz#paddle.Tensor | list[paddle.Tensor]) __future__rtypingrrrrr,rbase.data_feederr r base.frameworkr common_ops_importr rarrrcollections.abcr__all__rrr>rMrVrrrrrsY #88 C((::(  CL . . < <8v : : L LK\ 6:$$&$/3$$ $  &/B   &/<  dR8<F F4F)Fr