o p i.@sddlmZddlmZddlZddlZddlmZddlm Z ddl m Z m Z ddl mZmZdd lmZdd lmZmZd d lmZmZerNdd lmZgZd#ddZd#ddZe ddgid$ddZd$ddZd$ddZd%d&dd Zd%d&d!d"Z dS)') annotations) TYPE_CHECKINGN)_C_ops)ParamAliasDecorator) check_typecheck_variable_and_dtype)in_dynamic_or_pir_mode use_pir_api)Variable) LayerHelpercore)_complex_to_real_dtypeassign)TensorinputrreturncCs4t|dttjjfdt|j}tt |d}|S)a% Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor. Args: input (Tensor): The input Tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary. Returns: Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> input = paddle.rand((3, 100, 100)) >>> rank = paddle.rank(input) >>> print(rank.numpy()) 3 rint32) rr paddlepirValuelenshapernparray)rZndimsoutr^/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/paddle/tensor/attribute.pyrank%s rcCsltr t|}d|_|St|dgddtd it}|jdd}|jdd|id|idd d|_|S) a Get the shape of the input. .. code-block:: text Case1: Given N-D Tensor: input = [ [1, 2, 3, 4], [5, 6, 7, 8] ] Then: input.shape = [2, 4] Case2: Given SelectedRows: input.rows = [0, 4, 19] input.height = 20 input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor Then: input.shape = [3, 2] Args: input (Tensor): The input can be N-D Tensor or SelectedRows with data type bool, bfloat16, float16, float32, float64, int32, int64. If input variable is type of SelectedRows, returns the shape of it's inner tensor. Returns: Tensor: The shape of the input variable. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> paddle.enable_static() >>> inputs = paddle.static.data(name="x", shape=[3, 100, 100], dtype="float32") >>> output = paddle.shape(inputs) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) >>> img = np.ones((3, 100, 100)).astype(np.float32) >>> res = exe.run(paddle.static.default_main_program(), feed={'x': img}, fetch_list=[output]) >>> print(res) [array([ 3, 100, 100], dtype=int64)] Tr) booluint16Zfloat16Zfloat32Zfloat64rZint64 complex64 complex128r!Z float8_e4m3fnZ float8_e5m2rrdtypeZInputOut)typeinputsoutputs stop_gradientN)r) r rZshape64r*rr locals"create_variable_for_type_inference append_op)rrhelperrrrrAs(/  rxr cCsjt|tjtjjtjjfstdt||j }|t j j j kp2|t j j jkp2|t jj kp2|t jjk}|S)amReturn whether x is a tensor of complex data type(complex64 or complex128). .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``input=tensor_x`` is equivalent to ``x=tensor_x``. Args: x (Tensor): The input tensor. input: An alias for ``x`` , with identical behavior. Returns: bool: True if the data type of the input is complex data type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1 + 2j, 3 + 4j]) >>> print(paddle.is_complex(x)) True >>> x = paddle.to_tensor([1.1, 1.2]) >>> print(paddle.is_complex(x)) False >>> x = paddle.to_tensor([1, 2, 3]) >>> print(paddle.is_complex(x)) False )Expected Tensor, but received type of x: ) isinstancerrstaticr rr TypeErrorr'r%r VarDescVarTypeZ COMPLEX64Z COMPLEX128DataType)r/r%Zis_complex_dtyperrr is_complexs!   r7cCst|tjtjjtjjfstdt||j }|t j j j kpL|t j j jkpL|t j j jkpL|t j j jkpL|t jjkpL|t jjkpL|t jjkpL|t jjk}|S)aK Returns whether the dtype of `x` is one of paddle.float64, paddle.float32, paddle.float16, and paddle.bfloat16. Args: x (Tensor): The input tensor. Returns: bool: True if the dtype of `x` is floating type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1., 5., dtype='float32') >>> y = paddle.arange(1, 5, dtype='int32') >>> print(paddle.is_floating_point(x)) True >>> print(paddle.is_floating_point(y)) False r0)r1rrr2r rrr3r'r%r r4r5ZFP32ZFP64ZFP16ZBF16r6ZFLOAT32ZFLOAT64ZFLOAT16ZBFLOAT16)r/r%Z is_fp_dtyperrris_floating_points*        r8cCst|tjtjjtjjfstdt||j }d}t sC|t j j jkp@|t j j jkp@|t j j jkp@|t j j jkp@|t j j jk}|S|t jjkp`|t jjkp`|t jjkp`|t jjkp`|t jjk}|S)apReturn whether x is a tensor of integral data type. Args: x (Tensor): The input tensor. Returns: bool: True if the data type of the input is integer data type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1 + 2j, 3 + 4j]) >>> print(paddle.is_integer(x)) False >>> x = paddle.to_tensor([1.1, 1.2]) >>> print(paddle.is_integer(x)) False >>> x = paddle.to_tensor([1, 2, 3]) >>> print(paddle.is_integer(x)) True r0F)r1rrr2r rrr3r'r%r r r4r5ZUINT8ZINT8ZINT16ZINT32ZINT64r6)r/r%Z is_int_dtyperrr is_integers6        r9name str | NonecCbtrt|St|dddgdtd it}|jt|d}|j dd|id|id|S) a Returns a new Tensor containing real values of the input Tensor. Args: x (Tensor): the input Tensor, its data type could be complex64 or complex128. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: a Tensor containing real values of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]]) >>> print(x) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+6j), (2+5j), (3+4j)], [(4+3j), (5+2j), (6+1j)]]) >>> real_res = paddle.real(x) >>> print(real_res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) >>> real_t = x.real() >>> print(real_t) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) r/r"r#realr$Xr&r'r(r)N)r=) r rr=rr r+r,r input_dtyper-r/r:r.rrrrr= $  r=cCr<) a Returns a new tensor containing imaginary values of input tensor. Args: x (Tensor): the input tensor, its data type could be complex64 or complex128. name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: a tensor containing imaginary values of the input tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]]) >>> print(x) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+6j), (2+5j), (3+4j)], [(4+3j), (5+2j), (6+1j)]]) >>> imag_res = paddle.imag(x) >>> print(imag_res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[6., 5., 4.], [3., 2., 1.]]) >>> imag_t = x.imag() >>> print(imag_t) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[6., 5., 4.], [3., 2., 1.]]) r/r"r#imagr$r>r&r?N)rC) r rrCrr r+r,rr@r-rArrrrCPrBrC)rrrr)r/rrr )N)r/rr:r;rr)! __future__rtypingrnumpyrrrZpaddle.utils.decorator_utilsrZbase.data_feederrrZbase.frameworkr r Zcommon_ops_importr Z frameworkr r Zcreationrrr__all__rrr7r8r9r=rCrrrrs,         S  . ( 50