o !j@s8ddlZddlmZddlmZgZddZddZdS) N)core)DataToLoDTensorConvertercCst|tjrtt|||St|tr\t|t|gtj j j d}g}|D]}| t|| |q%|g|ks=Jdt|j}||jd}t}||||||St|tjr{t}||||||syJd|Std)a Create a LoDTensor from a numpy array, list or existing LoDTensor. The implementation is as follows: 1. Check whether the length-based LoD, i.e., :code:`recursive_seq_lens` is valid. 2. Convert :code:`recursive_seq_lens` to a offset-based LoD. 3. Based on :code:`place` , copy the :code:`data` from a numpy array, list or existing LoDTensor to CPU or GPU device. 4. Set offset-based LoD to the output LoDTensor. Suppose we want to create a LoDTensor to hold data for word sequences, where each word is represented by an integer. If we want to create a LoDTensor to represent two sentences, one of 2 words, and one of 3 words. Then :code:`data` would be a numpy array of integers with shape (5, 1). :code:`recursive_seq_lens` would be [[2, 3]], indicating the word number in each sentence. This length-based :code:`recursive_seq_lens` [[2, 3]] would be converted to offset-based LoD [[0, 2, 5]] inside the function call. Args: data (numpy.ndarray|list|LoDTensor): a numpy array, a list or ad LoDTensor holding the data to be copied. recursive_seq_lens (list[list[int]]): a list of lists indicating the length-based LoD info. place (CPUPlace|CUDAPlace): CPU or GPU place indicating where the data in the created LoDTensor will be stored. Returns: A LoDTensor with tensor data and recursive_seq_lens info. Examples: .. code-block:: python >>> import paddle.base as base >>> import numpy as np >>> t = base.create_lod_tensor(np.ndarray([5, 30]), [[2, 3]], base.CPUPlace()) )placeZ lod_levelshapeZdtypez(data and recursive_seq_lens do not match)rz the provided lod info is invalidz:data should be either a LoDTensor, a Numpy array or a list) isinstancerZ LoDTensorcreate_lod_tensornparraylistrlenZVarDescZVarTypeZFP32appendfeeddataZreshapersetZset_recursive_sequence_lengthsZndarrayZ$has_valid_recursive_sequence_lengths TypeError)rrecursive_seq_lensr converterZnew_recursive_seq_lensseqZarrZtensorrW/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/base/lod_tensor.pyrsJ /        rcCsFt|ts Jdt|dg|}tj|||d}t|||S)a :api_attr: Static Graph Create a LoDTensor containing random integers. The implementation is as follows: 1. Obtain the shape of output LoDTensor based on :code:`recursive_seq_lens` and :code:`base_shape` . The first dimension of the shape is the total length of sequences, while the other dimensions are the same as :code:`base_shape` . 2. Create a numpy array of random integers, and parse the created numpy array as parameter :code:`data` of :ref:`api_paddle_base_create_lod_tensor` to create the output LoDTensor. Suppose we want to create a LoDTensor to hold data for 2 sequences, where the dimension of the sequences are [2, 30] and [3, 30] respectively. The :code:`recursive_seq_lens` would be [[2, 3]], and :code:`base_shape` would be [30] (the other dimensions excluding the sequence length). Therefore, the shape of the output LoDTensor would be [5, 30], where the first dimension 5 is the total lengths of the sequences, and the other dimensions are :code:`base_shape`. Args: recursive_seq_lens (list[list[int]]): a list of lists indicating the length-based LoD info. base_shape (list[int]): the shape of the output LoDTensor excluding the first dimension. place (CPUPlace|CUDAPlace): CPU or GPU place indicating where the data in the created LoDTensor will be stored. low (int): the lower bound of the random integers. high (int): the upper bound of the random integers. Returns: A LoDTensor with tensor data and recursive_seq_lens info, whose data is inside [low, high]. Examples: .. code-block:: python >>> import paddle.base as base >>> t = base.create_random_int_lodtensor(recursive_seq_lens=[[2, 3]], ... base_shape=[30], place=base.CPUPlace(), low=0, high=10) >>> print(t.shape()) [5, 30] zbase_shape should be a listZint64)rr sumr randomZrandom_integersZastyper)rZ base_shaperlowhighZ overall_shaperrrrcreate_random_int_lodtensorss4 r) numpyr rZ data_feederr__all__rrrrrrs    \