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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import dygraph_only

__all__ = []


@dygraph_only
def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
    """
    Note:
        This API is only supported from ``CUDA 11.0`` .

    Applies matrix multiplication for `x` and `y` , `input` is added to
    the final result. The equation is:

    ..  math::

        Out = alpha * x * y + beta * input

    The supported input/output Tensor layout are as follows:

    Note:
        input[SparseCsrTensor] + x[SparseCsrTensor] @ y[SparseCsrTensor] -> out[SparseCsrTensor]
        input[DenseTensor] + x[SparseCsrTensor] @ y[DenseTensor] -> out[DenseTensor]
        input[SparseCooTensor] + x[SparseCooTensor] @ y[SparseCooTensor] -> out[SparseCooTensor]
        input[DenseTensor] + x[SparseCooTensor] @ y[DenseTensor] -> out[DenseTensor]

    It supports backward propagation.

    Dimensions `input` , `x` , `y` must be same and >= 2D. Automatic broadcasting of Tensor is not supported.

    Args:
        input (Tensor): The input tensor. Shape is [*, M, N]. The data type can be float32 or float64.
        x (Tensor): The input tensor. Shape is [*, M, K]. The data type can be float32 or float64.
        y (Tensor): The input tensor. Shape is [*, K, N]. The data type can be float32 or float64.
        beta (float, optional): Coefficient of `input` . Default: 1.0
        alpha (float, optional): Coefficient of `x * y` . Default: 1.0
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor: Its layout is determined by that of `x` and `y` . dtype and shape is the same with `input`

    Examples:

        .. code-block:: python

            import paddle

            # dense + csr @ dense -> dense
            input = paddle.rand([3, 2])
            crows = [0, 1, 2, 3]
            cols = [1, 2, 0]
            values = [1., 2., 3.]
            x = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
            y = paddle.rand([3, 2])
            out = paddle.incubate.sparse.addmm(input, x, y, 3.0, 2.0)

            # dense + coo @ dense -> dense
            input = paddle.rand([3, 2])
            indices = [[0, 1, 2], [1, 2, 0]]
            values = [1., 2., 3.]
            x = paddle.incubate.sparse.sparse_coo_tensor(indices, values, [3, 3])
            y = paddle.rand([3, 2])
            out = paddle.incubate.sparse.addmm(input, x, y, 3.0, 2.0)

    """
    return _C_ops.sparse_addmm(input, x, y, alpha, beta)