
    {-j                        d dl mZ d dlmZ d dlmZ ddlmZ erd dl	m
Z
 g Z G d de          Z G d	 d
e          Z G d de          Z G d de          ZdS )    )annotations)TYPE_CHECKING)Layer   )
functional)Tensorc                  6     e Zd ZdZdd fdZdd
ZddZ xZS )ReLUa~  

    Sparse ReLU Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        ReLU(x) = max(x, 0)

    Parameters:
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: Sparse Tensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.to_tensor([-2., 0., 1.])
            >>> sparse_x = dense_x.to_sparse_coo(1)
            >>> relu = paddle.sparse.nn.ReLU()
            >>> out = relu(sparse_x)
            >>> print(out)
            Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   indices=[[0, 2]],
                   values=[0., 1.])
    Nname
str | NonereturnNonec                V    t                                                       || _        d S Nsuper__init___nameselfr   	__class__s     a/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/paddle/sparse/nn/layer/activation.pyr   zReLU.__init__=   $    


    xr   c                6    t          j        || j                  S r   )Frelur   r   r   s     r   forwardzReLU.forwardA   s    va$$$r   strc                ,    | j         r
d| j          nd}|S Nzname= r   r   name_strs     r   
extra_reprzReLU.extra_reprD   #    +/:='4:'''2r   r   r   r   r   r   r   r   r   r   r   r!   __name__
__module____qualname____doc__r   r    r(   __classcell__r   s   @r   r
   r
      st         >      % % % %       r   r
   c                  6     e Zd ZdZdd fd
ZddZddZ xZS )SoftmaxaU  

    Sparse Softmax Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    Note:
        Only support axis=-1 for SparseCsrTensor, which is faster when read data
        by row (axis=-1).

    Transform x to dense matrix, and :math:`i` is row index, :math:`j` is column index.
    If axis=-1, We have:

    .. math::

        softmax_ij = \frac{\exp(x_ij - max_j(x_ij))}{\sum_j(exp(x_ij - max_j(x_ij))}

    Parameters:
        axis (int, optional): The axis along which to perform softmax calculations. Only support -1 for SparseCsrTensor.
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: SparseCooTensor / SparseCsrTensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle
            >>> paddle.seed(2022)

            >>> mask = paddle.rand((3, 4)) < 0.7
            >>> x = paddle.rand((3, 4)) * mask.astype('float32')
            >>> print(x)
            Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[0.88156885, 0.14463395, 0.17831714, 0.43818203],
             [0.07617740, 0.75576496, 0.        , 0.61921930],
             [0.        , 0.        , 0.42460245, 0.03001321]])

            >>> csr = x.to_sparse_csr()
            >>> print(csr)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   crows=[0, 4, 7, 9],
                   cols=[0, 1, 2, 3, 0, 1, 3, 2, 3],
                   values=[0.88156885, 0.14463395, 0.17831714, 0.43818203, 0.07617740,
                           0.75576496, 0.61921930, 0.42460245, 0.03001321])

            >>> softmax = paddle.sparse.nn.Softmax()
            >>> out = softmax(csr)
            >>> print(out)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   crows=[0, 4, 7, 9],
                   cols=[0, 1, 2, 3, 0, 1, 3, 2, 3],
                   values=[0.38234913, 0.18298410, 0.18925257, 0.24541418, 0.21302439,
                           0.42031071, 0.36666498, 0.59738696, 0.40261301])

            >>> coo = x.to_sparse_coo(sparse_dim=2)
            >>> print(coo)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   indices=[[0, 0, 0, 0, 1, 1, 1, 2, 2],
                            [0, 1, 2, 3, 0, 1, 3, 2, 3]],
                   values=[0.88156885, 0.14463395, 0.17831714, 0.43818203, 0.07617740,
                           0.75576496, 0.61921930, 0.42460245, 0.03001321])

            >>> out = softmax(coo)
            >>> print(out)
            Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(cpu), stop_gradient=True,
                   indices=[[0, 0, 0, 0, 1, 1, 1, 2, 2],
                            [0, 1, 2, 3, 0, 1, 3, 2, 3]],
                   values=[0.38234913, 0.18298411, 0.18925257, 0.24541420, 0.21302438,
                           0.42031071, 0.36666498, 0.59738696, 0.40261301])
    Naxisintr   r   r   r   c                d    t                                                       || _        || _        d S r   )r   r   _axisr   )r   r7   r   r   s      r   r   zSoftmax.__init__   s+    



r   r   r   c                B    t          j        || j        | j                  S r   )r   softmaxr:   r   r   s     r   r    zSoftmax.forward   s    yDJ
333r   r!   c                ,    | j         r
d| j          nd}|S r#   r%   r&   s     r   r(   zSoftmax.extra_repr   r)   r   )r6   N)r7   r8   r   r   r   r   r+   r,   r-   r3   s   @r   r5   r5   I   sw        F FP      
4 4 4 4       r   r5   c                  6     e Zd ZdZdd fdZdd
ZddZ xZS )ReLU6a  

    Sparse ReLU6 Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        ReLU6(x) = min(max(0,x), 6)

    Parameters:
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: Sparse Tensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.to_tensor([-2., 0., 8.])
            >>> sparse_x = dense_x.to_sparse_coo(1)
            >>> relu6 = paddle.sparse.nn.ReLU6()
            >>> out = relu6(sparse_x)
    Nr   r   r   r   c                V    t                                                       || _        d S r   r   r   s     r   r   zReLU6.__init__   r   r   r   r   c                6    t          j        || j                  S r   )r   relu6r   r   s     r   r    zReLU6.forward   s    wq$*%%%r   r!   c                ,    | j         r
d| j          nd}|S r#   r%   r&   s     r   r(   zReLU6.extra_repr   r)   r   r   r*   r+   r,   r-   r3   s   @r   r?   r?      st         6      & & & &       r   r?   c                  8     e Zd ZdZ	 dd fd
ZddZddZ xZS )	LeakyReLUa  

    Sparse Leaky ReLU Activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        LeakyReLU(x)=
            \left\{
                \begin{array}{rcl}
                    x, & & if \ x >= 0 \\
                    negative\_slope * x, & & otherwise \\
                \end{array}
            \right.

    Parameters:
        negative_slope (float, optional): Slope of the activation function at
            :math:`x < 0` . Default is 0.01.
        name (str|None, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
        - input: Sparse Tensor with any shape.
        - output: Sparse Tensor with the same shape as input.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.to_tensor([-2., 0., 5.])
            >>> sparse_x = dense_x.to_sparse_coo(1)
            >>> leaky_relu = paddle.sparse.nn.LeakyReLU(0.5)
            >>> out = leaky_relu(sparse_x)

    {Gz?Nnegative_slopefloatr   r   r   r   c                d    t                                                       || _        || _        d S r   )r   r   _negative_sloper   )r   rG   r   r   s      r   r   zLeakyReLU.__init__   s.     	-


r   r   r   c                B    t          j        || j        | j                  S r   )r   
leaky_relurJ   r   r   s     r   r    zLeakyReLU.forward   s    |At3TZ@@@r   r!   c                ,    | j         r
d| j          nd}|S r#   r%   r&   s     r   r(   zLeakyReLU.extra_repr   r)   r   )rF   N)rG   rH   r   r   r   r   r+   r,   r-   r3   s   @r   rE   rE      s        " "J @D      A A A A       r   rE   N)
__future__r   typingr   	paddle.nnr   r$   r   r   paddler   __all__r
   r5   r?   rE    r   r   <module>rT      s+   # " " " " "                         
) ) ) ) )5 ) ) )XS S S S Se S S Sl% % % % %E % % %P1 1 1 1 1 1 1 1 1 1r   