# 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.

import paddle.nn as nn


class Activation(nn.Layer):
    """
    The wrapper of activations.

    Args:
        act (str, optional): The activation name in lowercase. It must be one of ['elu', 'gelu',
            'hardshrink', 'tanh', 'hardtanh', 'prelu', 'relu', 'relu6', 'selu', 'leakyrelu', 'sigmoid',
            'softmax', 'softplus', 'softshrink', 'softsign', 'tanhshrink', 'logsigmoid', 'logsoftmax',
            'hsigmoid']. Default: None, means identical transformation.

    Returns:
        A callable object of Activation.

    Raises:
        KeyError: When parameter `act` is not in the optional range.

    Examples:

        from paddleseg.models.common.activation import Activation

        relu = Activation("relu")
        print(relu)
        # <class 'paddle.nn.layer.activation.ReLU'>

        sigmoid = Activation("sigmoid")
        print(sigmoid)
        # <class 'paddle.nn.layer.activation.Sigmoid'>

        not_exit_one = Activation("not_exit_one")
        # KeyError: "not_exit_one does not exist in the current dict_keys(['elu', 'gelu', 'hardshrink',
        # 'tanh', 'hardtanh', 'prelu', 'relu', 'relu6', 'selu', 'leakyrelu', 'sigmoid', 'softmax',
        # 'softplus', 'softshrink', 'softsign', 'tanhshrink', 'logsigmoid', 'logsoftmax', 'hsigmoid'])"
    """

    def __init__(self, act=None):
        super(Activation, self).__init__()

        self._act = act
        upper_act_names = nn.layer.activation.__dict__.keys()
        lower_act_names = [act.lower() for act in upper_act_names]
        act_dict = dict(zip(lower_act_names, upper_act_names))

        if act is not None:
            if act in act_dict.keys():
                act_name = act_dict[act]
                self.act_func = eval("nn.layer.activation.{}()".format(
                    act_name))
            else:
                raise KeyError("{} does not exist in the current {}".format(
                    act, act_dict.keys()))

    def forward(self, x):
        if self._act is not None:
            return self.act_func(x)
        else:
            return x