import math
import warnings

import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.cnn import (Conv2d, build_activation_layer, build_norm_layer,
                      constant_init, normal_init, trunc_normal_init)
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import MultiheadAttention
from mmcv.runner import BaseModule, ModuleList, Sequential, _load_checkpoint

from .transformer_helper import PatchEmbed, nchw_to_nlc, nlc_to_nchw, resize, \
                                get_root_logger, BaseDecodeHead, HEADS, BACKBONES


class MixFFN(BaseModule):
    """An implementation of MixFFN of Segformer.

    The differences between MixFFN & FFN:
        1. Use 1X1 Conv to replace Linear layer.
        2. Introduce 3X3 Conv to encode positional information.

    Args:
        embed_dims (int): The feature dimension. Same as
            `MultiheadAttention`. Defaults: 256.
        feedforward_channels (int): The hidden dimension of FFNs.
            Defaults: 1024.
        act_cfg (dict, optional): The activation config for FFNs.
            Default: dict(type='ReLU')
        ffn_drop (float, optional): Probability of an element to be
            zeroed in FFN. Default 0.0.
        dropout_layer (obj:`ConfigDict`): The dropout_layer used
            when adding the shortcut.
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
    """

    def __init__(self,
                 embed_dims,
                 feedforward_channels,
                 act_cfg=dict(type='GELU'),
                 ffn_drop=0.,
                 dropout_layer=None,
                 init_cfg=None):
        super(MixFFN, self).__init__(init_cfg)

        self.embed_dims = embed_dims
        self.feedforward_channels = feedforward_channels
        self.act_cfg = act_cfg
        self.activate = build_activation_layer(act_cfg)

        in_channels = embed_dims
        fc1 = Conv2d(
            in_channels=in_channels,
            out_channels=feedforward_channels,
            kernel_size=1,
            stride=1,
            bias=True)
        # 3x3 depth wise conv to provide positional encode information
        pe_conv = Conv2d(
            in_channels=feedforward_channels,
            out_channels=feedforward_channels,
            kernel_size=3,
            stride=1,
            padding=(3 - 1) // 2,
            bias=True,
            groups=feedforward_channels)
        fc2 = Conv2d(
            in_channels=feedforward_channels,
            out_channels=in_channels,
            kernel_size=1,
            stride=1,
            bias=True)
        drop = nn.Dropout(ffn_drop)
        layers = [fc1, pe_conv, self.activate, drop, fc2, drop]
        self.layers = Sequential(*layers)
        self.dropout_layer = build_dropout(
            dropout_layer) if dropout_layer else torch.nn.Identity()

    def forward(self, x, hw_shape, identity=None):
        out = nlc_to_nchw(x, hw_shape)
        out = self.layers(out)
        out = nchw_to_nlc(out)
        if identity is None:
            identity = x
        return identity + self.dropout_layer(out)


class EfficientMultiheadAttention(MultiheadAttention):
    """An implementation of Efficient Multi-head Attention of Segformer.

    This module is modified from MultiheadAttention which is a module from
    mmcv.cnn.bricks.transformer.

    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        attn_drop (float): A Dropout layer on attn_output_weights.
            Default: 0.0.
        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
            Default: 0.0.
        dropout_layer (obj:`ConfigDict`): The dropout_layer used
            when adding the shortcut. Default: None.
        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
            Default: None.
        batch_first (bool): Key, Query and Value are shape of
            (batch, n, embed_dim)
            or (n, batch, embed_dim). Default: False.
        qkv_bias (bool): enable bias for qkv if True. Default True.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN').
        sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
            Attention of Segformer. Default: 1.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 attn_drop=0.,
                 proj_drop=0.,
                 dropout_layer=None,
                 init_cfg=None,
                 batch_first=True,
                 qkv_bias=False,
                 norm_cfg=dict(type='LN'),
                 sr_ratio=1):
        super().__init__(
            embed_dims,
            num_heads,
            attn_drop,
            proj_drop,
            dropout_layer=dropout_layer,
            init_cfg=init_cfg,
            batch_first=batch_first,
            bias=qkv_bias)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = Conv2d(
                in_channels=embed_dims,
                out_channels=embed_dims,
                kernel_size=sr_ratio,
                stride=sr_ratio)
            # The ret[0] of build_norm_layer is norm name.
            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]

    def forward(self, x, hw_shape, identity=None):

        x_q = x
        if self.sr_ratio > 1:
            x_kv = nlc_to_nchw(x, hw_shape)
            x_kv = self.sr(x_kv)
            x_kv = nchw_to_nlc(x_kv)
            x_kv = self.norm(x_kv)
        else:
            x_kv = x

        if identity is None:
            identity = x_q

        # `need_weights=True` will let nn.MultiHeadAttention
        # `return attn_output, attn_output_weights.sum(dim=1) / num_heads`
        # The `attn_output_weights.sum(dim=1)` may cause cuda error. So, we set
        # `need_weights=False` to ignore `attn_output_weights.sum(dim=1)`.
        # This issue - `https://github.com/pytorch/pytorch/issues/37583` report
        # the error that large scale tensor sum operation may cause cuda error.
        out = self.attn(query=x_q, key=x_kv, value=x_kv, need_weights=False)[0]

        return identity + self.dropout_layer(self.proj_drop(out))


class TransformerEncoderLayer(BaseModule):
    """Implements one encoder layer in Segformer.

    Args:
        embed_dims (int): The feature dimension.
        num_heads (int): Parallel attention heads.
        feedforward_channels (int): The hidden dimension for FFNs.
        drop_rate (float): Probability of an element to be zeroed.
            after the feed forward layer. Default 0.0.
        attn_drop_rate (float): The drop out rate for attention layer.
            Default 0.0.
        drop_path_rate (float): stochastic depth rate. Default 0.0.
        qkv_bias (bool): enable bias for qkv if True.
            Default: True.
        act_cfg (dict): The activation config for FFNs.
            Defalut: dict(type='GELU').
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN').
        batch_first (bool): Key, Query and Value are shape of
            (batch, n, embed_dim)
            or (n, batch, embed_dim). Default: False.
        init_cfg (dict, optional): Initialization config dict.
            Default:None.
        sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
            Attention of Segformer. Default: 1.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 qkv_bias=True,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 batch_first=True,
                 sr_ratio=1):
        super(TransformerEncoderLayer, self).__init__()

        # The ret[0] of build_norm_layer is norm name.
        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]

        self.attn = EfficientMultiheadAttention(
            embed_dims=embed_dims,
            num_heads=num_heads,
            attn_drop=attn_drop_rate,
            proj_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            batch_first=batch_first,
            qkv_bias=qkv_bias,
            norm_cfg=norm_cfg,
            sr_ratio=sr_ratio)

        # The ret[0] of build_norm_layer is norm name.
        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]

        self.ffn = MixFFN(
            embed_dims=embed_dims,
            feedforward_channels=feedforward_channels,
            ffn_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            act_cfg=act_cfg)

    def forward(self, x, hw_shape):
        x = self.attn(self.norm1(x), hw_shape, identity=x)
        x = self.ffn(self.norm2(x), hw_shape, identity=x)
        return x


@BACKBONES.register_module()
class MixVisionTransformer(BaseModule):
    """The backbone of Segformer.

    A PyTorch implement of : `SegFormer: Simple and Efficient Design for
    Semantic Segmentation with Transformers` -
        https://arxiv.org/pdf/2105.15203.pdf

    Args:
        in_channels (int): Number of input channels. Default: 3.
        embed_dims (int): Embedding dimension. Default: 768.
        num_stags (int): The num of stages. Default: 4.
        num_layers (Sequence[int]): The layer number of each transformer encode
            layer. Default: [3, 4, 6, 3].
        num_heads (Sequence[int]): The attention heads of each transformer
            encode layer. Default: [1, 2, 4, 8].
        patch_sizes (Sequence[int]): The patch_size of each overlapped patch
            embedding. Default: [7, 3, 3, 3].
        strides (Sequence[int]): The stride of each overlapped patch embedding.
            Default: [4, 2, 2, 2].
        sr_ratios (Sequence[int]): The spatial reduction rate of each
            transformer encode layer. Default: [8, 4, 2, 1].
        out_indices (Sequence[int] | int): Output from which stages.
            Default: (0, 1, 2, 3).
        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
            Default: 4.
        qkv_bias (bool): Enable bias for qkv if True. Default: True.
        drop_rate (float): Probability of an element to be zeroed.
            Default 0.0
        attn_drop_rate (float): The drop out rate for attention layer.
            Default 0.0
        drop_path_rate (float): stochastic depth rate. Default 0.0
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN')
        act_cfg (dict): The activation config for FFNs.
            Defalut: dict(type='GELU').
        pretrain_style (str): Choose to use official or mmcls pretrain weights.
            Default: official.
        pretrained (str, optional): model pretrained path. Default: None.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None.
    """

    def __init__(self,
                 in_channels=64,
                 embed_dims=64,
                 num_stages=4,
                 num_layers=[3, 4, 6, 3],
                 num_heads=[1, 2, 4, 8],
                 patch_sizes=[7, 3, 3, 3],
                 strides=[2, 2, 2, 2],
                 sr_ratios=[8, 4, 2, 1],
                 out_indices=(0, 1, 2, 3),
                 mlp_ratio=4,
                 qkv_bias=True,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN', eps=1e-6),
                 pretrain_style='official',
                 pretrained=None,
                 init_cfg=None):
        super().__init__()

        assert pretrain_style in [
            'official', 'mmcls'
        ], 'we only support official weights or mmcls weights.'

        if isinstance(pretrained, str) or pretrained is None:
            warnings.warn('DeprecationWarning: pretrained is a deprecated, '
                          'please use "init_cfg" instead')
        else:
            raise TypeError('pretrained must be a str or None')

        self.embed_dims = embed_dims

        self.num_stages = num_stages
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.patch_sizes = patch_sizes
        self.strides = strides
        self.sr_ratios = sr_ratios
        assert num_stages == len(num_layers) == len(num_heads) \
            == len(patch_sizes) == len(strides) == len(sr_ratios)

        self.out_indices = out_indices
        assert max(out_indices) < self.num_stages
        self.pretrain_style = pretrain_style
        self.pretrained = pretrained
        self.init_cfg = init_cfg

        # transformer encoder
        dpr = [
            x.item()
            for x in torch.linspace(0, drop_path_rate, sum(num_layers))
        ]  # stochastic num_layer decay rule

        cur = 0
        self.layers = ModuleList()
        for i, num_layer in enumerate(num_layers):
            embed_dims_i = embed_dims * num_heads[i]
            patch_embed = PatchEmbed(
                in_channels=in_channels,
                embed_dims=embed_dims_i,
                kernel_size=patch_sizes[i],
                stride=strides[i],
                padding=patch_sizes[i] // 2,
                pad_to_patch_size=False,
                norm_cfg=norm_cfg)
            layer = ModuleList([
                TransformerEncoderLayer(
                    embed_dims=embed_dims_i,
                    num_heads=num_heads[i],
                    feedforward_channels=mlp_ratio * embed_dims_i,
                    drop_rate=drop_rate,
                    attn_drop_rate=attn_drop_rate,
                    drop_path_rate=dpr[cur + idx],
                    qkv_bias=qkv_bias,
                    act_cfg=act_cfg,
                    norm_cfg=norm_cfg,
                    sr_ratio=sr_ratios[i]) for idx in range(num_layer)
            ])
            in_channels = embed_dims_i
            # The ret[0] of build_norm_layer is norm name.
            norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
            self.layers.append(ModuleList([patch_embed, layer, norm]))
            cur += num_layer

    def init_weights(self):
        if self.pretrained is None:
            for m in self.modules():
                if isinstance(m, nn.Linear):
                    trunc_normal_init(m.weight, std=.02)
                    if m.bias is not None:
                        constant_init(m.bias, 0)
                elif isinstance(m, nn.LayerNorm):
                    constant_init(m.bias, 0)
                    constant_init(m.weight, 1.0)
                elif isinstance(m, nn.Conv2d):
                    fan_out = m.kernel_size[0] * m.kernel_size[
                        1] * m.out_channels
                    fan_out //= m.groups
                    normal_init(m.weight, 0, math.sqrt(2.0 / fan_out))
                    if m.bias is not None:
                        constant_init(m.bias, 0)
        elif isinstance(self.pretrained, str):
            logger = get_root_logger()
            checkpoint = _load_checkpoint(
                self.pretrained, logger=logger, map_location='cpu')
            if 'state_dict' in checkpoint:
                state_dict = checkpoint['state_dict']
            else:
                state_dict = checkpoint


            # only use this code if when adopt v3
            ori_proj_weight = state_dict['layers.0.0.projection.weight']
            state_dict['layers.0.0.projection.weight'] = torch.cat([ori_proj_weight, ori_proj_weight], dim=1)

            self.load_state_dict(state_dict, True)


    def forward(self, x, additional_features=None):

        outs = []
        for i, layer in enumerate(self.layers):
            x, H, W = layer[0](x), layer[0].DH, layer[0].DW
            hw_shape = (H, W)
            for block in layer[1]:
                x = block(x, hw_shape)
            x = layer[2](x)
            x = nlc_to_nchw(x, hw_shape)
            if i in self.out_indices:
                outs.append(x)

        return outs


@HEADS.register_module()
class SegformerHead(BaseDecodeHead):
    """The all mlp Head of segformer.

    This head is the implementation of
    `Segformer <https://arxiv.org/abs/2105.15203>` _.

    Args:
        interpolate_mode: The interpolate mode of MLP head upsample operation.
            Default: 'bilinear'.
    """

    def __init__(self, interpolate_mode='bilinear', **kwargs):
        super().__init__(input_transform='multiple_select', **kwargs)

        self.interpolate_mode = interpolate_mode
        num_inputs = len(self.in_channels)

        assert num_inputs == len(self.in_index)

        self.convs = nn.ModuleList()
        for i in range(num_inputs):
            self.convs.append(
                ConvModule(
                    in_channels=self.in_channels[i],
                    out_channels=self.channels,
                    kernel_size=1,
                    stride=1,
                    norm_cfg=self.norm_cfg,
                    act_cfg=self.act_cfg))

        self.fusion_conv = ConvModule(
            in_channels=self.channels * num_inputs,
            out_channels=self.channels,
            kernel_size=1,
            norm_cfg=self.norm_cfg)

    def forward(self, inputs):
        # Receive 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32
        inputs = self._transform_inputs(inputs)
        outs = []
        for idx in range(len(inputs)):
            x = inputs[idx]
            conv = self.convs[idx]
            outs.append(
                resize(
                    input=conv(x),
                    size=inputs[0].shape[2:],
                    mode=self.interpolate_mode,
                    align_corners=self.align_corners))

        out = self.fusion_conv(torch.cat(outs, dim=1))

        out = self.cls_seg(out)

        return out
