#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from ...fluid.initializer import XavierInitializer

__all__ = []


class XavierNormal(XavierInitializer):
    r"""
    This class implements the Xavier weight initializer from the paper
    `Understanding the difficulty of training deep feedforward neural
    networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
    by Xavier Glorot and Yoshua Bengio, using a normal distribution whose mean is :math:`0` and standard deviation is

    .. math::

        \sqrt{\frac{2.0}{fan\_in + fan\_out}}.


    Args:
        fan_in (float, optional): fan_in for Xavier initialization, which is
                inferred from the Tensor. The default value is None.
        fan_out (float, optional): fan_out for Xavier initialization, which is
                 inferred from the Tensor. The default value is None.
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.

    Returns:
        A parameter initialized by Xavier weight, using a normal distribution.

    Examples:
        .. code-block:: python
            :name: initializer_XavierNormal-example

            import paddle

            data = paddle.ones(shape=[3, 1, 2], dtype='float32')
            weight_attr = paddle.framework.ParamAttr(
                name="linear_weight",
                initializer=paddle.nn.initializer.XavierNormal())
            bias_attr = paddle.framework.ParamAttr(
                name="linear_bias",
                initializer=paddle.nn.initializer.XavierNormal())
            linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
            # inear.weight:  [[ 0.06910077 -0.18103665]
            #                 [-0.02546741 -1.0402188 ]]
            # linear.bias:  [-0.5012929   0.12418364]

            res = linear(data)
            # res:  [[[-0.4576595 -1.0970719]]
            #        [[-0.4576595 -1.0970719]]
            #        [[-0.4576595 -1.0970719]]]
    """

    def __init__(self, fan_in=None, fan_out=None, name=None):
        super(XavierNormal, self).__init__(uniform=False,
                                           fan_in=fan_in,
                                           fan_out=fan_out,
                                           seed=0)


class XavierUniform(XavierInitializer):
    r"""
    This class implements the Xavier weight initializer from the paper
    `Understanding the difficulty of training deep feedforward neural
    networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
    by Xavier Glorot and Yoshua Bengio.

    This initializer is designed to keep the scale of the gradients
    approximately same in all the layers. In case of Uniform distribution,
    the range is :math:`[-x,x]`, where

    .. math::

        x = \sqrt{\frac{6.0}{fan\_in + fan\_out}}.

    Args:
        fan_in (float, optional): fan_in for Xavier initialization, which is
                inferred from the Tensor. The default value is None.
        fan_out (float, optional): fan_out for Xavier initialization, which is
                 inferred from the Tensor. The default value is None.
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.

    Returns:
        A parameter initialized by Xavier weight, using a uniform distribution.

    Examples:
        .. code-block:: python
            :name: initializer_XavierUniform-example

            import paddle

            data = paddle.ones(shape=[3, 1, 2], dtype='float32')
            weight_attr = paddle.framework.ParamAttr(
                name="linear_weight",
                initializer=paddle.nn.initializer.XavierUniform())
            bias_attr = paddle.framework.ParamAttr(
                name="linear_bias",
                initializer=paddle.nn.initializer.XavierUniform())
            linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
            # linear.weight:  [[-0.04229349 -1.1248565 ]
            #                  [-0.10789523 -0.5938053 ]]
            # linear.bias:  [ 1.1983747  -0.40201235]

            res = linear(data)
            # res:  [[[ 1.0481861 -2.1206741]]
            #        [[ 1.0481861 -2.1206741]]
            #        [[ 1.0481861 -2.1206741]]]
    """

    def __init__(self, fan_in=None, fan_out=None, name=None):
        super(XavierUniform, self).__init__(uniform=True,
                                            fan_in=fan_in,
                                            fan_out=fan_out,
                                            seed=0)