# Copyright 2023 lyuwenyu
#
# 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
#
#     https://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 copy
from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F

from .activate import get_activation


class ConvNormLayer(nn.Module):
    def __init__(
        self,
        ch_in,
        ch_out,
        kernel_size,
        stride,
        padding=None,
        bias=False,
        act=None,
    ):
        super().__init__()
        self.conv = nn.Conv2d(
            ch_in,
            ch_out,
            kernel_size,
            stride,
            padding=(kernel_size - 1) // 2 if padding is None else padding,
            bias=bias,
        )
        self.norm = nn.BatchNorm2d(ch_out)
        self.act = nn.Identity() if act is None else get_activation(act)

    def forward(self, x):
        return self.act(self.norm(self.conv(x)))


class TransformerEncoder(nn.Module):
    def __init__(self, encoder_layer, num_layers, norm=None):
        super(TransformerEncoder, self).__init__()
        self.layers = nn.ModuleList(
            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
        )
        self.num_layers = num_layers
        self.norm = norm

    def forward(self, src, src_mask=None, pos_embed=None) -> torch.Tensor:
        output = src
        for layer in self.layers:
            output = layer(output, src_mask=src_mask, pos_embed=pos_embed)

        if self.norm is not None:
            output = self.norm(output)

        return output


class TransformerEncoderLayer(nn.Module):
    def __init__(
        self,
        d_model,
        nhead,
        dim_feedforward=2048,
        dropout=0.1,
        activation="relu",
        normalize_before=False,
    ):
        super().__init__()
        self.normalize_before = normalize_before

        self.self_attn = nn.MultiheadAttention(
            d_model, nhead, dropout, batch_first=True
        )

        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.activation = get_activation(activation)

    @staticmethod
    def with_pos_embed(tensor, pos_embed):
        return tensor if pos_embed is None else tensor + pos_embed

    def forward(self, src, src_mask=None, pos_embed=None) -> torch.Tensor:
        residual = src
        if self.normalize_before:
            src = self.norm1(src)
        q = k = self.with_pos_embed(src, pos_embed)
        src, _ = self.self_attn(q, k, value=src, attn_mask=src_mask)

        src = residual + self.dropout1(src)
        if not self.normalize_before:
            src = self.norm1(src)

        residual = src
        if self.normalize_before:
            src = self.norm2(src)
        src = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = residual + self.dropout2(src)
        if not self.normalize_before:
            src = self.norm2(src)
        return src


class RepVggBlock(nn.Module):
    def __init__(self, ch_in, ch_out, act="relu"):
        super().__init__()
        self.ch_in = ch_in
        self.ch_out = ch_out
        self.conv1 = ConvNormLayer(ch_in, ch_out, 3, 1, padding=1, act=None)
        self.conv2 = ConvNormLayer(ch_in, ch_out, 1, 1, padding=0, act=None)
        self.act = nn.Identity() if act is None else get_activation(act)

    def forward(self, x):
        if hasattr(self, "conv"):
            y = self.conv(x)
        else:
            y = self.conv1(x) + self.conv2(x)

        return self.act(y)

    def convert_to_deploy(self):
        if not hasattr(self, "conv"):
            self.conv = nn.Conv2d(self.ch_in, self.ch_out, 3, 1, padding=1)

        kernel, bias = self.get_equivalent_kernel_bias()
        self.conv.weight.data = kernel
        self.conv.bias.data = bias

    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.conv1)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.conv2)

        return (
            kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1),
            bias3x3 + bias1x1,
        )

    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return F.pad(kernel1x1, [1, 1, 1, 1])

    def _fuse_bn_tensor(self, branch: ConvNormLayer):
        if branch is None:
            return 0, 0
        kernel = branch.conv.weight
        running_mean = branch.norm.running_mean
        running_var = branch.norm.running_var
        gamma = branch.norm.weight
        beta = branch.norm.bias
        eps = branch.norm.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std


class CSPRepLayer(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        num_blocks=3,
        expansion=1.0,
        bias=None,
        act="silu",
    ):
        super(CSPRepLayer, self).__init__()
        hidden_channels = int(out_channels * expansion)
        self.conv1 = ConvNormLayer(
            in_channels, hidden_channels, 1, 1, bias=bias, act=act
        )
        self.conv2 = ConvNormLayer(
            in_channels, hidden_channels, 1, 1, bias=bias, act=act
        )
        self.bottlenecks = nn.Sequential(
            *[
                RepVggBlock(hidden_channels, hidden_channels, act=act)
                for _ in range(num_blocks)
            ]
        )
        if hidden_channels != out_channels:
            self.conv3 = ConvNormLayer(
                hidden_channels, out_channels, 1, 1, bias=bias, act=act
            )
        else:
            self.conv3 = nn.Identity()

    def forward(self, x):
        x_1 = self.conv1(x)
        x_1 = self.bottlenecks(x_1)
        x_2 = self.conv2(x)
        return self.conv3(x_1 + x_2)


class HybridEncoder(nn.Module):
    __share__ = [
        "eval_spatial_size",
    ]

    def __init__(
        self,
        in_channels=[512, 1024, 2048],
        feat_strides=[8, 16, 32],
        hidden_dim=256,
        nhead=8,
        dim_feedforward=1024,
        dropout=0.0,
        enc_act="gelu",
        use_encoder_idx=[2],
        num_encoder_layers=1,
        pe_temperature=10000,
        expansion=1.0,
        depth_mult=1.0,
        act="silu",
        eval_spatial_size=None,
        version="v2",
    ):
        super().__init__()
        self.in_channels = in_channels
        self.feat_strides = feat_strides
        self.hidden_dim = hidden_dim
        self.use_encoder_idx = use_encoder_idx
        self.num_encoder_layers = num_encoder_layers
        self.pe_temperature = pe_temperature
        self.eval_spatial_size = eval_spatial_size
        self.out_channels = [hidden_dim for _ in range(len(in_channels))]
        self.out_strides = feat_strides

        # channel projection
        self.input_proj = nn.ModuleList()
        for in_channel in in_channels:
            if version == "v1":
                proj = nn.Sequential(
                    nn.Conv2d(in_channel, hidden_dim, kernel_size=1, bias=False),
                    nn.BatchNorm2d(hidden_dim),
                )
            elif version == "v2":
                proj = nn.Sequential(
                    OrderedDict(
                        [
                            (
                                "conv",
                                nn.Conv2d(
                                    in_channel,
                                    hidden_dim,
                                    kernel_size=1,
                                    bias=False,
                                ),
                            ),
                            ("norm", nn.BatchNorm2d(hidden_dim)),
                        ]
                    )
                )
            else:
                raise AttributeError()

            self.input_proj.append(proj)

        # encoder transformer
        encoder_layer = TransformerEncoderLayer(
            hidden_dim,
            nhead=nhead,
            dim_feedforward=dim_feedforward,
            dropout=dropout,
            activation=enc_act,
        )

        self.encoder = nn.ModuleList(
            [
                TransformerEncoder(copy.deepcopy(encoder_layer), num_encoder_layers)
                for _ in range(len(use_encoder_idx))
            ]
        )

        # top-down fpn
        self.lateral_convs = nn.ModuleList()
        self.fpn_blocks = nn.ModuleList()
        for _ in range(len(in_channels) - 1, 0, -1):
            self.lateral_convs.append(
                ConvNormLayer(hidden_dim, hidden_dim, 1, 1, act=act)
            )
            self.fpn_blocks.append(
                CSPRepLayer(
                    hidden_dim * 2,
                    hidden_dim,
                    round(3 * depth_mult),
                    act=act,
                    expansion=expansion,
                )
            )

        # bottom-up pan
        self.downsample_convs = nn.ModuleList()
        self.pan_blocks = nn.ModuleList()
        for _ in range(len(in_channels) - 1):
            self.downsample_convs.append(
                ConvNormLayer(hidden_dim, hidden_dim, 3, 2, act=act)
            )
            self.pan_blocks.append(
                CSPRepLayer(
                    hidden_dim * 2,
                    hidden_dim,
                    round(3 * depth_mult),
                    act=act,
                    expansion=expansion,
                )
            )

        self._reset_parameters()

    def _reset_parameters(self):
        if self.eval_spatial_size:
            for idx in self.use_encoder_idx:
                stride = self.feat_strides[idx]
                pos_embed = self.build_2d_sincos_position_embedding(
                    self.eval_spatial_size[1] // stride,
                    self.eval_spatial_size[0] // stride,
                    self.hidden_dim,
                    self.pe_temperature,
                )
                setattr(self, f"pos_embed{idx}", pos_embed)
                # self.register_buffer(f'pos_embed{idx}', pos_embed)

    @staticmethod
    def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0):
        """ """
        grid_w = torch.arange(int(w), dtype=torch.float32)
        grid_h = torch.arange(int(h), dtype=torch.float32)
        grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
        assert (
            embed_dim % 4 == 0
        ), "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
        pos_dim = embed_dim // 4
        omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
        omega = 1.0 / (temperature**omega)

        out_w = grid_w.flatten()[..., None] @ omega[None]
        out_h = grid_h.flatten()[..., None] @ omega[None]

        return torch.concat(
            [out_w.sin(), out_w.cos(), out_h.sin(), out_h.cos()], dim=1
        )[None, :, :]

    def forward(self, feats):
        assert len(feats) == len(self.in_channels)
        proj_feats = [self.input_proj[i](feat) for i, feat in enumerate(feats)]

        # encoder
        if self.num_encoder_layers > 0:
            for i, enc_ind in enumerate(self.use_encoder_idx):
                h, w = proj_feats[enc_ind].shape[2:]
                # flatten [B, C, H, W] to [B, HxW, C]
                src_flatten = proj_feats[enc_ind].flatten(2).permute(0, 2, 1)
                if self.training or self.eval_spatial_size is None:
                    pos_embed = self.build_2d_sincos_position_embedding(
                        w, h, self.hidden_dim, self.pe_temperature
                    ).to(src_flatten.device)
                else:
                    pos_embed = getattr(self, f"pos_embed{enc_ind}", None).to(
                        src_flatten.device
                    )

                memory: torch.Tensor = self.encoder[i](src_flatten, pos_embed=pos_embed)
                proj_feats[enc_ind] = (
                    memory.permute(0, 2, 1)
                    .reshape(-1, self.hidden_dim, h, w)
                    .contiguous()
                )

        # broadcasting and fusion
        inner_outs = [proj_feats[-1]]
        for idx in range(len(self.in_channels) - 1, 0, -1):
            feat_heigh = inner_outs[0]
            feat_low = proj_feats[idx - 1]
            feat_heigh = self.lateral_convs[len(self.in_channels) - 1 - idx](feat_heigh)
            inner_outs[0] = feat_heigh
            upsample_feat = F.interpolate(feat_heigh, scale_factor=2.0, mode="nearest")
            inner_out = self.fpn_blocks[len(self.in_channels) - 1 - idx](
                torch.concat([upsample_feat, feat_low], dim=1)
            )
            inner_outs.insert(0, inner_out)

        outs = [inner_outs[0]]
        for idx in range(len(self.in_channels) - 1):
            feat_low = outs[-1]
            feat_height = inner_outs[idx + 1]
            downsample_feat = self.downsample_convs[idx](feat_low)
            out = self.pan_blocks[idx](
                torch.concat([downsample_feat, feat_height], dim=1)
            )
            outs.append(out)

        return outs


# model = HybridEncoder(
#    in_channels=[512, 1024, 2048],
#    feat_strides=[8, 16, 32],
#    hidden_dim=256,
#    use_encoder_idx=[2],
#    num_encoder_layers=1,
#    nhead=8,
#    dim_feedforward=1024,
#    dropout=0.0,
#    enc_act="gelu",
#    expansion=1.0,
#    depth_mult=1.0,
#    act="silu",
# )

# print(model)
