import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
from timm.models.vision_transformer import Block as TimmBlock
from timm.models.vision_transformer import Attention as TimmAttention

from lib.models.tools.module_helper import ModuleHelper


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.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

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


class GroupAttention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., ws=1):
        assert ws != 1
        super(GroupAttention, self).__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.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.ws = ws

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.view(B, H, W, C)
        pad_l = pad_t = 0
        pad_r = (self.ws - W % self.ws) % self.ws
        pad_b = (self.ws - H % self.ws) % self.ws
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
        _, Hp, Wp, _ = x.shape
        _h, _w = Hp // self.ws, Wp // self.ws
        mask = torch.zeros((1, Hp, Wp), device=x.device)
        mask[:, -pad_b:, :].fill_(1)
        mask[:, :, -pad_r:].fill_(1)

        x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)  # B, _h, _w, ws, ws, C
        mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1, _h * _w, self.ws * self.ws)
        attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3)  # 1, _h*_w, ws*ws, ws*ws
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0))
        qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads,
                                  C // self.num_heads).permute(3, 0, 1, 4, 2, 5)  # n_h, B, _w*_h, nhead, ws*ws, dim
        q, k, v = qkv[0], qkv[1], qkv[2]  # B, _h*_w, n_head, ws*ws, dim_head
        attn = (q @ k.transpose(-2, -1)) * self.scale  # B, _h*_w, n_head, ws*ws, ws*ws
        attn = attn + attn_mask.unsqueeze(2)
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)  # attn @v ->  B, _h*_w, n_head, ws*ws, dim_head
        attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
        x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
        if pad_r > 0 or pad_b > 0:
            x = x[:, :H, :W, :].contiguous()
        x = x.reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        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.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        else:
            kv = self.kv(x).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
        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 GroupBlock(TimmBlock):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=1):
        super(GroupBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop,
                                         drop_path, act_layer, norm_layer)
        del self.attn
        if ws == 1:
            self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, sr_ratio)
        else:
            self.attn = GroupAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, ws)

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \
            f"img_size {img_size} should be divided by patch_size {patch_size}."
        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        B, C, H, W = x.shape

        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        H, W = H // self.patch_size[0], W // self.patch_size[1]

        return x, (H, W)


# PEG  from https://arxiv.org/abs/2102.10882
class PosCNN(nn.Module):
    def __init__(self, in_chans, embed_dim=768, s=1):
        super(PosCNN, self).__init__()
        self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, s, 1, bias=True, groups=embed_dim), )
        self.s = s

    def forward(self, x, H, W):
        B, N, C = x.shape
        feat_token = x
        cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
        if self.s == 1:
            x = self.proj(cnn_feat) + cnn_feat
        else:
            x = self.proj(cnn_feat)
        x = x.flatten(2).transpose(1, 2)
        return x

    def no_weight_decay(self):
        return ['proj.%d.weight' % i for i in range(4)]


class PyramidVisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], block_cls=GroupBlock, wss=[7,7,7]):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths
        self.wss = wss

        # patch_embed
        self.patch_embeds = nn.ModuleList()
        self.pos_embeds = nn.ParameterList()
        self.pos_drops = nn.ModuleList()
        self.blocks = nn.ModuleList()

        for i in range(len(depths)):
            if i == 0:
                self.patch_embeds.append(PatchEmbed(img_size, patch_size, in_chans, embed_dims[i]))
            else:
                self.patch_embeds.append(
                    PatchEmbed(img_size // patch_size // 2 ** (i - 1), 2, embed_dims[i - 1], embed_dims[i]))
            self.pos_drops.append(nn.Dropout(p=drop_rate))

        self.pos_block = nn.ModuleList(
            [PosCNN(embed_dim, embed_dim) for embed_dim in embed_dims]
        )

        # transformer encoder
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0
        self.blocks = nn.ModuleList()
        for k in range(len(depths)):
            _block = nn.ModuleList([block_cls(
                    dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
                    sr_ratio=sr_ratios[k], ws=1 if i % 2 == 1 else wss[k]) for i in range(depths[k])])
            self.blocks.append(_block)
            cur += depths[k]
        self.apply(self._init_weights)

    def no_weight_decay(self):
        return set(['pos_block.' + n for n, p in self.pos_block.named_parameters()])

    def reset_drop_path(self, drop_path_rate):
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
        cur = 0
        for k in range(len(self.depths)):
            for i in range(self.depths[k]):
                self.blocks[k][i].drop_path.drop_prob = dpr[cur + i]
            cur += self.depths[k]

    def _init_weights(self, m):
        import math
        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_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1.0)
            m.bias.data.zero_()

    def forward_features(self, x):
        outs = []
        B = x.shape[0]

        for i in range(len(self.depths)):
            x, (H, W) = self.patch_embeds[i](x)
            x = self.pos_drops[i](x)
            for j, blk in enumerate(self.blocks[i]):
                x = blk(x, H, W)
                if j == 0:
                    x = self.pos_block[i](x, H, W)
            x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()

            outs.append(x)

        return outs

    def forward(self, x):
        outs = self.forward_features(x)

        return outs


def _conv_filter(state_dict, patch_size=16):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k:
            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
        out_dict[k] = v

    return out_dict


def svt_small(configer, **kwargs):
    img_size = configer.get('train', 'data_transformer')['input_size'][0]
    num_classes = configer.get('data', 'num_classes')
    model = PyramidVisionTransformer(img_size=img_size, num_classes=num_classes,
                                     patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16],
                                     mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 10, 4],
                                     sr_ratios=[8, 4, 2, 1], drop_rate=0.0,
                                     drop_path_rate=0.2,
                                     wss=[7, 7, 7, 7],
                                     **kwargs)

    return model


def svt_base(configer, **kwargs):
    img_size = configer.get('train', 'data_transformer')['input_size'][0]
    num_classes = configer.get('data', 'num_classes')
    model = PyramidVisionTransformer(img_size=img_size, num_classes=num_classes,
                                     patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24],
                                     mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 18, 2],
                                     sr_ratios=[8, 4, 2, 1],
                                     wss=[7, 7, 7, 7],
                                     drop_path_rate=0.2,
                                     **kwargs)

    return model


def svt_large(configer, **kwargs):
    img_size = configer.get('train', 'data_transformer')['input_size'][0]
    num_classes = configer.get('data', 'num_classes')
    model = PyramidVisionTransformer(img_size=img_size, num_classes=num_classes,
                                     patch_size=4, embed_dims=[128, 256, 512, 1024], num_heads=[4, 8, 16, 32],
                                     mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
                                     norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 18, 2],
                                     sr_ratios=[8, 4, 2, 1],
                                     wss=[7, 7, 7, 7],
                                     drop_path_rate=0.3,
                                     **kwargs)

    return model


class SVTBackbone(object):
    def __init__(self, configer):
        self.configer = configer

    def __call__(self):
        arch = self.configer.get('network', 'backbone')

        if arch == 'svt_small':
            model = svt_small(configer=self.configer)
        elif arch == 'svt_base':
            model = svt_base(configer=self.configer)
        elif arch == 'svt_large':
            model = svt_large(configer=self.configer)

        model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'),
                                        all_match=False, network="svt")

        return model