import torch
import torch.nn as nn
import math
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from .position import PositionEmbeddingSine

def window_partition(x, window_size):
    B, H, W, C = x.shape
    x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
    windows = (
        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0] * window_size[1], C)
    )
    return windows


def window_reverse(windows, window_size, H, W):
    nwB, N, C = windows.shape
    windows = windows.view(-1, window_size[0], window_size[1], C)
    B = int(nwB / (H * W / window_size[0] / window_size[1]))
    x = windows.view(
        B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1
    )
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


def pad_if_needed(x, size, window_size):
    n, h, w, c = size
    pad_h = math.ceil(h / window_size[0]) * window_size[0] - h
    pad_w = math.ceil(w / window_size[1]) * window_size[1] - w
    if pad_h > 0 or pad_w > 0:  # center-pad the feature on H and W axes
        img_mask = torch.zeros((1, h + pad_h, w + pad_w, 1))  # 1 H W 1
        h_slices = (
            slice(0, pad_h // 2),
            slice(pad_h // 2, h + pad_h // 2),
            slice(h + pad_h // 2, None),
        )
        w_slices = (
            slice(0, pad_w // 2),
            slice(pad_w // 2, w + pad_w // 2),
            slice(w + pad_w // 2, None),
        )
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        mask_windows = window_partition(
            img_mask, window_size
        )  # nW, window_size*window_size, 1
        mask_windows = mask_windows.squeeze(-1)
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(
            attn_mask != 0, float(-100.0)
        ).masked_fill(attn_mask == 0, float(0.0))
        return nn.functional.pad(
            x,
            (0, 0, pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2),
        ), attn_mask
    return x, None


def depad_if_needed(x, size, window_size):
    n, h, w, c = size
    pad_h = math.ceil(h / window_size[0]) * window_size[0] - h
    pad_w = math.ceil(w / window_size[1]) * window_size[1] - w
    if pad_h > 0 or pad_w > 0:  # remove the center-padding on feature
        return x[:, pad_h // 2: pad_h // 2 + h, pad_w // 2: pad_w // 2 + w, :].contiguous()
    return x


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
        self.relu = nn.ReLU(inplace=True)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(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
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        x = self.fc1(x)
        x = self.dwconv(x, H, W)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class InterFrameAttention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(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
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x1, x2, H, W, mask=None):
        B, N, C = x1.shape
        q = self.q(x1).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        kv = self.kv(x2).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]
        attn = (q @ k.transpose(-2, -1)) * self.scale

        if mask is not None:
            nW = mask.shape[0]  # mask: nW, N, N
            attn = attn.view(B // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
                1
            ).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = attn.softmax(dim=-1)
        else:
            attn = attn.softmax(dim=-1)

        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class MotionFormerBlock(nn.Module):
    def __init__(self, dim, num_heads, window_size=0, shift_size=0, mlp_ratio=4., bidirectional=True,
                 qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, ):
        super().__init__()
        self.window_size = window_size
        if not isinstance(self.window_size, (tuple, list)):
            self.window_size = to_2tuple(window_size)
        self.shift_size = shift_size
        if not isinstance(self.shift_size, (tuple, list)):
            self.shift_size = to_2tuple(shift_size)
        self.bidirectional = bidirectional
        self.norm1 = norm_layer(dim)
        self.attn = InterFrameAttention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        # BEGIN: absolute pos_embed, beneficial to local information extraction in our experiments
        self.pos_embed = PositionEmbeddingSine(dim // 2)
        # END: absolute pos_embed
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(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
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W, B, self_att=False):
        x = x.view(2 * B, H, W, -1)
        x_pad, mask = pad_if_needed(x, x.size(), self.window_size)

        if self.shift_size[0] or self.shift_size[1]:
            _, H_p, W_p, C = x_pad.shape
            x_pad = torch.roll(x_pad, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))

            if hasattr(self, 'HW') and self.HW.item() == H_p * W_p:
                shift_mask = self.attn_mask
            else:
                shift_mask = torch.zeros((1, H_p, W_p, 1))  # 1 H W 1
                h_slices = (slice(0, -self.window_size[0]),
                            slice(-self.window_size[0], -self.shift_size[0]),
                            slice(-self.shift_size[0], None))
                w_slices = (slice(0, -self.window_size[1]),
                            slice(-self.window_size[1], -self.shift_size[1]),
                            slice(-self.shift_size[1], None))
                cnt = 0
                for h in h_slices:
                    for w in w_slices:
                        shift_mask[:, h, w, :] = cnt
                        cnt += 1

                mask_windows = window_partition(shift_mask, self.window_size).squeeze(-1)
                shift_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
                shift_mask = shift_mask.masked_fill(shift_mask != 0,
                                                    float(-100.0)).masked_fill(shift_mask == 0,
                                                                               float(0.0))

                if mask is not None:
                    shift_mask = shift_mask.masked_fill(mask != 0,
                                                        float(-100.0))
                self.register_buffer("attn_mask", shift_mask)
                self.register_buffer("HW", torch.Tensor([H_p * W_p]))
        else:
            shift_mask = mask

        if shift_mask is not None:
            shift_mask = shift_mask.to(x_pad.device)

        _, Hw, Ww, C = x_pad.shape
        x_win = window_partition(x_pad, self.window_size)

        nwB = x_win.shape[0]
        x_norm = self.norm1(x_win)
        # BEGIN: absolute pos embed, beneficial to local information extraction in our experiments
        x_norm = x_norm.view(nwB, self.window_size[0], self.window_size[1], C).permute(0, 3, 1, 2)
        ape = self.pos_embed(x_norm)
        x_norm = x_norm + ape
        x_norm = x_norm.permute(0, 2, 3, 1).view(nwB, self.window_size[0] * self.window_size[1], C)
        # END: absolute pos embed

        if self_att is False:
            x_reverse = torch.cat([x_norm[nwB // 2:], x_norm[:nwB // 2]])
            x_appearence = self.attn(x_norm, x_reverse, H, W, shift_mask)
        else:
            x_appearence = self.attn(x_norm, x_norm, H, W, shift_mask)

        x_norm = x_norm + self.drop_path(x_appearence)

        x_back = x_norm
        x_back_win = window_reverse(x_back, self.window_size, Hw, Ww)

        if self.shift_size[0] or self.shift_size[1]:
            x_back_win = torch.roll(x_back_win, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2))

        x = depad_if_needed(x_back_win, x.size(), self.window_size).view(2 * B, H * W, -1)

        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
        return x


class ConvBlock(nn.Module):
    def __init__(self, in_dim, out_dim, depths=2, act_layer=nn.PReLU):
        super().__init__()
        layers = []
        for i in range(depths):
            if i == 0:
                layers.append(nn.Conv2d(in_dim, out_dim, 3, 1, 1))
            else:
                layers.append(nn.Conv2d(out_dim, out_dim, 3, 1, 1))
            layers.extend([
                act_layer(out_dim),
            ])
        self.conv = nn.Sequential(*layers)

    def _init_weights(self, m):
        if isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = self.conv(x)
        return x


class OverlapPatchEmbed(nn.Module):
    def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
        super().__init__()
        patch_size = to_2tuple(patch_size)

        self.patch_size = patch_size
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
                              padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.norm = nn.LayerNorm(embed_dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(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
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)

        return x, H, W


class MotionFormer(nn.Module):
    def __init__(self, in_chans=3, embed_dims=None, num_heads=None,
                 mlp_ratios=None, qkv_bias=True, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=None, window_sizes=None, **kwarg):
        super().__init__()
        self.depths = depths
        self.num_stages = len(embed_dims)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0

        self.conv_stages = self.num_stages - len(num_heads)

        for i in range(self.num_stages):
            if i == 0:
                block = ConvBlock(in_chans, embed_dims[i], depths[i])
            else:
                if i < self.conv_stages:
                    patch_embed = nn.Sequential(
                        nn.Conv2d(embed_dims[i - 1], embed_dims[i], 3, 2, 1),
                        nn.PReLU(embed_dims[i])
                    )
                    block = ConvBlock(embed_dims[i], embed_dims[i], depths[i])
                else:
                    patch_embed = OverlapPatchEmbed(patch_size=3,
                                                    stride=2,
                                                    in_chans=embed_dims[i - 1],
                                                    embed_dim=embed_dims[i])

                    block = nn.ModuleList([MotionFormerBlock(
                        dim=embed_dims[i], num_heads=num_heads[i - self.conv_stages],
                        window_size=window_sizes[i - self.conv_stages],
                        shift_size=0 if (j % 2) == 0 else window_sizes[i - self.conv_stages] // 2,
                        mlp_ratio=mlp_ratios[i - self.conv_stages], qkv_bias=qkv_bias, qk_scale=qk_scale,
                        drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer)
                        for j in range(depths[i])])

                    norm = norm_layer(embed_dims[i])
                    setattr(self, f"norm{i + 1}", norm)
                setattr(self, f"patch_embed{i + 1}", patch_embed)
            cur += depths[i]

            setattr(self, f"block{i + 1}", block)

        self.cor = {}

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(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
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def get_cor(self, shape, device):
        k = (str(shape), str(device))
        if k not in self.cor:
            tenHorizontal = torch.linspace(-1.0, 1.0, shape[2], device=device).view(
                1, 1, 1, shape[2]).expand(shape[0], -1, shape[1], -1).permute(0, 2, 3, 1)
            tenVertical = torch.linspace(-1.0, 1.0, shape[1], device=device).view(
                1, 1, shape[1], 1).expand(shape[0], -1, -1, shape[2]).permute(0, 2, 3, 1)
            self.cor[k] = torch.cat([tenHorizontal, tenVertical], -1).to(device)
        return self.cor[k]

    def forward(self, x1, x2):
        B = x1.shape[0]
        x = torch.cat([x1, x2], 0)
        appearence_features = []
        xs = []
        for i in range(self.num_stages):
            patch_embed = getattr(self, f"patch_embed{i + 1}", None)
            block = getattr(self, f"block{i + 1}", None)
            norm = getattr(self, f"norm{i + 1}", None)
            if i < self.conv_stages:
                if i > 0:
                    x = patch_embed(x)
                x = block(x)
                xs.append(x)
            else:
                x, H, W = patch_embed(x)
                for j in range(len(block)):
                    x = block[j](x, H, W, B, self_att=False)
                    xs.append(x.reshape(2 * B, H, W, -1).permute(0, 3, 1, 2).contiguous())
                x = norm(x)
                x = x.reshape(2 * B, H, W, -1).permute(0, 3, 1, 2).contiguous()
            appearence_features.append(x)
        return appearence_features


class DWConv(nn.Module):
    def __init__(self, dim):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).reshape(B, C, H, W).contiguous()
        x = self.dwconv(x)
        x = x.reshape(B, C, -1).transpose(1, 2)

        return x


def feature_extractor(**kargs):
    model = MotionFormer(**kargs)
    return model