from abc import ABCMeta, abstractmethod

import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmcv.runner import auto_fp16

from additional_modules.segformer.ops import resize


class BaseDecodeHead(nn.Module, metaclass=ABCMeta):
    """Base class for BaseDecodeHead.

    Args:
        in_channels (int|Sequence[int]): Input channels.
        channels (int): Channels after modules, before conv_seg.
        num_classes (int): Number of classes.
        dropout_ratio (float): Ratio of dropout layer. Default: 0.1.
        conv_cfg (dict|None): Config of conv layers. Default: None.
        norm_cfg (dict|None): Config of norm layers. Default: None.
        act_cfg (dict): Config of activation layers.
            Default: dict(type='ReLU')
        in_index (int|Sequence[int]): Input feature index. Default: -1
        input_transform (str|None): Transformation type of input features.
            Options: 'resize_concat', 'multiple_select', None.
            'resize_concat': Multiple feature maps will be resize to the
                same size as first one and than concat together.
                Usually used in FCN head of HRNet.
            'multiple_select': Multiple feature maps will be bundle into
                a list and passed into decode head.
            None: Only one select feature map is allowed.
            Default: None.
        ignore_index (int | None): The label index to be ignored. When using
            masked BCE loss, ignore_index should be set to None. Default: 255
        sampler (dict|None): The config of segmentation map sampler.
            Default: None.
        align_corners (bool): align_corners argument of F.interpolate.
            Default: False.
    """

    def __init__(self,
                 in_channels,
                 channels,
                 *,
                 num_classes,
                 dropout_ratio=0.1,
                 conv_cfg=None,
                 norm_cfg=None,
                 act_cfg=dict(type='ReLU'),
                 in_index=-1,
                 input_transform=None,
                 decoder_params=None,
                 ignore_index=255,
                 align_corners=False):
        super(BaseDecodeHead, self).__init__()
        self._init_inputs(in_channels, in_index, input_transform)
        self.channels = channels
        self.num_classes = num_classes
        self.dropout_ratio = dropout_ratio
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
        self.in_index = in_index
        self.ignore_index = ignore_index
        self.align_corners = align_corners
        self.sampler = None

        self.conv_seg = nn.Conv2d(channels, num_classes, kernel_size=1)
        if dropout_ratio > 0:
            self.dropout = nn.Dropout2d(dropout_ratio)
        else:
            self.dropout = None
        self.fp16_enabled = False

    def extra_repr(self):
        """Extra repr."""
        s = f'input_transform={self.input_transform}, ' \
            f'ignore_index={self.ignore_index}, ' \
            f'align_corners={self.align_corners}'
        return s

    def _init_inputs(self, in_channels, in_index, input_transform):
        """Check and initialize input transforms.

        The in_channels, in_index and input_transform must match.
        Specifically, when input_transform is None, only single feature map
        will be selected. So in_channels and in_index must be of type int.
        When input_transform

        Args:
            in_channels (int|Sequence[int]): Input channels.
            in_index (int|Sequence[int]): Input feature index.
            input_transform (str|None): Transformation type of input features.
                Options: 'resize_concat', 'multiple_select', None.
                'resize_concat': Multiple feature maps will be resize to the
                    same size as first one and than concat together.
                    Usually used in FCN head of HRNet.
                'multiple_select': Multiple feature maps will be bundle into
                    a list and passed into decode head.
                None: Only one select feature map is allowed.
        """

        if input_transform is not None:
            assert input_transform in ['resize_concat', 'multiple_select']
        self.input_transform = input_transform
        self.in_index = in_index
        if input_transform is not None:
            assert isinstance(in_channels, (list, tuple))
            assert isinstance(in_index, (list, tuple))
            assert len(in_channels) == len(in_index)
            if input_transform == 'resize_concat':
                self.in_channels = sum(in_channels)
            else:
                self.in_channels = in_channels
        else:
            assert isinstance(in_channels, int)
            assert isinstance(in_index, int)
            self.in_channels = in_channels

    def init_weights(self):
        """Initialize weights of classification layer."""
        normal_init(self.conv_seg, mean=0, std=0.01)

    def _transform_inputs(self, inputs):
        """Transform inputs for decoder.

        Args:
            inputs (list[Tensor]): List of multi-level img features.

        Returns:
            Tensor: The transformed inputs
        """

        if self.input_transform == 'resize_concat':
            inputs = [inputs[i] for i in self.in_index]
            upsampled_inputs = [
                resize(
                    input=x,
                    size=inputs[0].shape[2:],
                    mode='bilinear',
                    align_corners=self.align_corners) for x in inputs
            ]
            inputs = torch.cat(upsampled_inputs, dim=1)
        elif self.input_transform == 'multiple_select':
            inputs = [inputs[i] for i in self.in_index]
        else:
            inputs = inputs[self.in_index]

        return inputs

    @auto_fp16()
    @abstractmethod
    def forward(self, inputs):
        """Placeholder of forward function."""
        pass

    def cls_seg(self, feat):
        """Classify each pixel."""
        if self.dropout is not None:
            feat = self.dropout(feat)
        output = self.conv_seg(feat)
        return output