# Copyright (c) 2021 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 os

import paddle
import paddle.nn as nn
import paddle.nn.functional as F


def SyncBatchNorm(*args, **kwargs):
    """In cpu environment nn.SyncBatchNorm does not have kernel so use nn.BatchNorm2D instead"""
    if paddle.get_device() == 'cpu' or os.environ.get('PADDLESEG_EXPORT_STAGE'):
        return nn.BatchNorm2D(*args, **kwargs)
    else:
        return nn.SyncBatchNorm(*args, **kwargs)


class ConvBNReLU(nn.Layer):
    """Basic conv bn relu layer."""

    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, padding: str = 'same', **kwargs):
        super().__init__()

        self._conv = nn.Conv2D(in_channels, out_channels, kernel_size, padding=padding, **kwargs)

        self._batch_norm = SyncBatchNorm(out_channels)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self._conv(x)
        x = self._batch_norm(x)
        x = F.relu(x)
        return x


class ConvBN(nn.Layer):
    """Basic conv bn layer."""

    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, padding: str = 'same', **kwargs):
        super().__init__()
        self._conv = nn.Conv2D(in_channels, out_channels, kernel_size, padding=padding, **kwargs)
        self._batch_norm = SyncBatchNorm(out_channels)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self._conv(x)
        x = self._batch_norm(x)
        return x


class ConvReLUPool(nn.Layer):
    """Basic conv bn pool layer."""

    def __init__(self, in_channels: int, out_channels: int):
        super().__init__()
        self.conv = nn.Conv2D(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self.conv(x)
        x = F.relu(x)
        x = F.pool2d(x, pool_size=2, pool_type="max", pool_stride=2)
        return x


class SeparableConvBNReLU(nn.Layer):
    """Basic separable conv bn relu layer."""

    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, padding: str = 'same', **kwargs):
        super().__init__()
        self.depthwise_conv = ConvBN(
            in_channels,
            out_channels=in_channels,
            kernel_size=kernel_size,
            padding=padding,
            groups=in_channels,
            **kwargs)
        self.piontwise_conv = ConvBNReLU(in_channels, out_channels, kernel_size=1, groups=1)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self.depthwise_conv(x)
        x = self.piontwise_conv(x)
        return x


class DepthwiseConvBN(nn.Layer):
    """Basic depthwise conv bn relu layer."""

    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, padding: str = 'same', **kwargs):
        super().__init__()
        self.depthwise_conv = ConvBN(
            in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            padding=padding,
            groups=in_channels,
            **kwargs)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self.depthwise_conv(x)
        return x


class AuxLayer(nn.Layer):
    """
    The auxiliary layer implementation for auxiliary loss.

    Args:
        in_channels (int): The number of input channels.
        inter_channels (int): The intermediate channels.
        out_channels (int): The number of output channels, and usually it is num_classes.
        dropout_prob (float, optional): The drop rate. Default: 0.1.
    """

    def __init__(self, in_channels: int, inter_channels: int, out_channels: int, dropout_prob: float = 0.1):
        super().__init__()

        self.conv_bn_relu = ConvBNReLU(in_channels=in_channels, out_channels=inter_channels, kernel_size=3, padding=1)

        self.dropout = nn.Dropout(p=dropout_prob)

        self.conv = nn.Conv2D(in_channels=inter_channels, out_channels=out_channels, kernel_size=1)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self.conv_bn_relu(x)
        x = self.dropout(x)
        x = self.conv(x)
        return x


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: str = 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: paddle.Tensor) -> paddle.Tensor:
        if self._act is not None:
            return self.act_func(x)
        else:
            return x