# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

import paddle
import paddle.nn as nn
import paddle.nn.initializer as paddle_init

__all__ = [
    'to_2tuple', 'DropPath', 'Identity', 'trunc_normal_', 'zeros_', 'ones_',
    'init_weights'
]


def to_2tuple(x):
    return tuple([x] * 2)


def drop_path(x, drop_prob=0., training=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = paddle.to_tensor(1 - drop_prob)
    shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
    random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
    random_tensor = paddle.floor(random_tensor)  # binarize
    output = x.divide(keep_prob) * random_tensor
    return output


class DropPath(nn.Layer):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Identity(nn.Layer):
    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, input):
        return input


trunc_normal_ = paddle_init.TruncatedNormal(std=.02)
zeros_ = paddle_init.Constant(value=0.)
ones_ = paddle_init.Constant(value=1.)


def init_weights(layer):
    """
    Init the weights of transformer.
    Args:
        layer(nn.Layer): The layer to init weights.
    Returns:
        None
    """
    if isinstance(layer, nn.Linear):
        trunc_normal_(layer.weight)
        if layer.bias is not None:
            zeros_(layer.bias)
    elif isinstance(layer, nn.LayerNorm):
        zeros_(layer.bias)
        ones_(layer.weight)