o #j.@spddlZddlmZmZddlmZddlmZddlm Z m Z m Z gZ ddZ d d Zdd d Zdd dZdS)N) check_typecheck_variable_and_dtype) in_pir_mode)Variable) LayerHelpercorein_dynamic_modecCstrt|ts Jdt|Str(t|tjjr|s"t dtj |St|t r5|j tjjjkr9t dtd it}|jdd}d|_|jdd|gid |gid |S) a5 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 LOD_TENSOR_ARRAY. Returns: Tensor: 1-D Tensor with shape [1], 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 vairable in array_length Op array_lengthint64dtypeTZlod_array_lengthXOuttypeZinputsZoutputsN)r )r isinstancelistlenrpaddlepirOpResultis_dense_tensor_array_type TypeError_pir_opsr rrrVarDescVarTypeLOD_TENSOR_ARRAYrlocals"create_variable_for_type_inferenceZ stop_gradient append_op)arrayhelpertmpr&T/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/tensor/array.pyr s>   r cCstr(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 ``LOD_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 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 or LoDTensor. Data type: float32, float64, int32, int64 and bool. i (Tensor): 1-D Tensor with shape [1], 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 ``LOD_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 moder(r)rNr zNThe index 'i' should not be greater than the length of 'array' in dygraph moder*r array_writez'x should be pir.OpResult, but recevied .r,xz7array should be tensor array vairable in array_write Op.outnamerrZwrite_to_arrayr-rr)r2)r rrr/r0 create_arrayrrrappendrrrrrrrrr array_write_rrr rrrrcreate_variabler7r")r4r*r#r$r&r&r'r2s        r2cCsg}|durt|ttfstdt|t|}|D]}t|ttjj fs0tdt|qt r6|St rat|t j jt jfsJtjj|}tj|}|D] }tj||t|}qR|Stdit}|j|jdt j jj|d}|D] }t|t||dqz|S) aR This OP creates an array. It is used as the input of :ref:`api_paddle_tensor_array_array_read` and :ref:`api_paddle_tensor_array_array_write`. Args: dtype (str): The data type of the elements in the array. Support data type: float32, float64, int32, int64 and bool. initialized_list(list): Used to initialize as default value for created array. All values in initialized list should be a Tensor. Returns: list|Tensor: An empty array. In dynamic mode, ``array`` is a Python list. But in static graph mode, array is a Tensor whose ``VarType`` is ``LOD_TENSOR_ARRAY``. Examples: .. code-block:: python >>> import paddle >>> arr = paddle.tensor.create_array(dtype="float32") >>> x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32") >>> i = paddle.zeros(shape=[1], dtype="int32") >>> arr = paddle.tensor.array_write(x, i, array=arr) >>> item = paddle.tensor.array_read(arr, i) >>> print(item.numpy()) [[5. 5. 5.]] NzDRequire type(initialized_list) should be list/tuple, but received {}zUAll values in `initialized_list` should be Variable or pir.OpResult, but recevied {}.r#r5r6)r4r*r#)r#)rrtuplerformatrrrrrr rrrrZDataTypebase frameworkZconvert_np_dtype_to_dtype_rr8r:r rr r;r7rr2)rZinitialized_listr#valr1r$Z tensor_arrayr&r&r'r8sF  r8)N)rZbase.data_feederrrZbase.frameworkrZcommon_ops_importrr?rrr __all__r r+r2r8r&r&r&r's  ; Nc