o + i2@s$ddlmZddlmZmZmZmZddlZddlmZddl m Z m Z ddl m Z ddlmZdd lmZmZmZerBdd lmZgZed Zed+ddZed,ddZddZed-ddZed.ddZddZe d/d0ddZed1d!dZed2d"dZ d/d#dZ d/d3d)d*ZdS)4) annotations) TYPE_CHECKINGAnyTypeVaroverloadN)_typing) check_typecheck_variable_and_dtype) in_pir_mode)Variable) LayerHelpercorein_dynamic_mode)SequenceTarray list[Any]returnintcCdSNrrr_/home/app/PaddleOCR-VL-test/.venv_paddleocr/lib/python3.10/site-packages/paddle/tensor/array.py array_length#r paddle.TensorcCrrrrrrrr'rcCstrt|ts Jdt|Str(t|tjjr|s"t dtj |St|t r5|j tjjjkr9t dtd it}|jdd}d|_|jdd|gid |gid |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 Oprint64dtypeTZlod_array_lengthXOuttypeZinputsZoutputsN)r)r isinstancelistlenr paddlepirValueis_dense_tensor_array_type TypeError_pir_opsrr r&rVarDescVarTypeDENSE_TENSOR_ARRAYr locals"create_variable_for_type_inferenceZ stop_gradient append_op)rhelpertmprrrr+s:    list[T]icCrrrrr9rrr array_readfrr;cCrrrr:rrrr;jrcCstr(t|ts Jdt|tsJd|jdgksJd|d}||StrAt|tjj r6| s:t dtj ||St|ddgd tdit}t|tr^|jtjjjkrbt d |j|jd }|jd |g|gd d|gid|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 moder<r=rNrzNThe index 'i' should not be greater than the length of 'array' in dygraph moder9r rHz$x should be pir.Value, but received .r>rDz7array should be tensor array variable in array_write Op.outnamer&r"Zwrite_to_arrayr?r$r%)rH)#rr'r rArB create_arrayr"r(r)appendr r r*r+r,r.r&r-r/base libpaddleDataType UNDEFINEDcast array_write_r r r3rr0r1r2create_variablerNr5)rDr9rr6rrrrHs|$          r"_typing.DTypeLikeinitialized_listSequence[paddle.Tensor] | None#paddle.Tensor | list[paddle.Tensor]cCs.g}|durt|ttfstdt|t|}|D]}t|ttjjfs1tdt|dqt r7|St rpt|t j j t jfsKtjj|}tj|}|D]}|tjjjjkrct||}tj||t|qS|Std it}|j|jdt j j j|d}|D] }t|t||dq|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 rKrrLrMrGr)r'r(tupler.r&r r*r+r,rr rr0r1rSrQ frameworkZconvert_np_dtype_to_dtype_r/rOrRrTrUrVrr r3rWrNr2rH)r"rYrvalrCr6Z tensor_arrayrrrrO5sB!    rO)rrrr)rrrr)rr8r9rrr)rrr9rrrr)rDrr9rrrErrF)rDrr9rrrJrrJ)rDrr9rrrrr)r"rXrYrZrr[) __future__rtypingrrrrr*rZbase.data_feederr r Zbase.frameworkr Zcommon_ops_importr r]r rrcollections.abcr__all__rrr;rHrOrrrrsB       ;  N   i