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 d dlmZ er<d dlmZ d dlmZmZ d d	lmZ g Z	
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S )     )annotations)TYPE_CHECKINGLiteral)_C_ops)in_dynamic_or_pir_mode)_update_padding_nd)convert_to_list)Tensor)Size3Size6)_PaddingSizeModeNFNDHWCxr	   kernel_sizer
   strideSize3 | Nonepadding _PaddingSizeMode | Size3 | Size6	ceil_modebooldata_formatLiteral['NDHWC']name
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      C  s   t  sJ d|  sJ d|dksJ dt|dd}|du r$|}nt|dd}d	}t|d||d
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    Implements sparse max pooling 3d operation.
    See more details in :ref:`api_paddle_sparse_nn_MaxPool3D` .

    Args:
        x (Tensor): The input SparseCooTensor of pooling operator, which is a 5-D tensor with
                          shape [N, D, H, W, C]. The format of input tensor `"NDHWC"`, where N represents batch size, C represents the number of channels, D, H and W represent the depth, height and width of the feature respectively.
        kernel_size (int|list|tuple): The pool kernel size. If the kernel size
            is a tuple or list, it must contain three integers,
            (kernel_size_Depth, kernel_size_Height, kernel_size_Width).
            Otherwise, the pool kernel size will be the cube of an int.
        stride (int|list|tuple, optional): The pool stride size. If pool stride size is a tuple or list,
            it must contain three integers, [stride_Depth, stride_Height, stride_Width).
            Otherwise, the pool stride size will be a cube of an int.
        padding (string|int|list|tuple, optional): The padding size. Padding could be in one of the following forms.
            1. A string in ['valid', 'same'].
            2. An int, which means the feature map is zero padded by size of `padding` on every sides.
            3. A list[int] or tuple(int) whose length is 3, [pad_depth, pad_height, pad_weight] whose value means the padding size of each dimension.
            4. A list[int] or tuple(int) whose length is 6. [pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side.
            5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
            The default value is 0.
        ceil_mode (bool, optional): ${ceil_mode_comment}
        data_format (string, optional): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
                        The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
                        `[batch_size, input_channels, input_depth, input_height, input_width]`. Currently only support `"NDHWC"` .
        name(str|None, optional): For detailed information, please refer
                             to :ref:`api_guide_Name`. Usually name is no need to set and
                             None by default.

    Returns:
        Tensor: The output tensor of pooling result. The data type is same as input tensor.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> dense_x = paddle.randn((1, 4, 4, 4, 3))
            >>> sparse_x = dense_x.to_sparse_coo(4)
            >>> kernel_sizes = [3, 3, 3]
            >>> paddings = [0, 0, 0]
            >>> strides = [1, 1, 1]
            >>> out = paddle.sparse.nn.functional.max_pool3d(sparse_x, kernel_sizes, stride=strides, padding=paddings)
            >>> print(out.shape)
            [1, 2, 2, 2, 3]
    z<Currently, Sparse API only support dynamic mode or pir mode.z@Currently, sparse.relu only support the input of SparseCooTensorr   z@Currently, sparse.max_pool3d only support data format of 'NDHWC'   Z	pool_sizeNZpool_strideT)channel_lastr   )   r   r   )r   Zis_sparse_coor   r   r   Zsparse_maxpool)
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__future__r   typingr   r   Zpaddler   Zpaddle.frameworkr   Zpaddle.nn.functional.poolingr   Zpaddle.utilsr   r	   Zpaddle._typingr
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