import numpy as np
import torch
import torch.nn as nn
from torch.nn.init import kaiming_normal_, ones_, trunc_normal_, zeros_

from openrec.modeling.common import DropPath, Identity, Mlp


class ConvBNLayer(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size=3,
        stride=1,
        padding=0,
        bias=False,
        groups=1,
        act=nn.GELU,
    ):
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            bias=bias,
        )
        self.norm = nn.BatchNorm2d(out_channels)
        self.act = act()

    def forward(self, inputs):
        out = self.conv(inputs)
        out = self.norm(out)
        out = self.act(out)
        return out


class Attention(nn.Module):

    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.dim = dim
        self.head_dim = dim // num_heads
        self.scale = qk_scale or self.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)

    def forward(self, x):
        B, N, _ = x.shape
        qkv = (self.qkv(x).reshape(B, N, 3, self.num_heads,
                                   self.head_dim).permute(2, 0, 3, 1, 4))
        q, k, v = qkv.unbind(0)
        attn = q @ k.transpose(-2, -1) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = attn @ v
        x = x.transpose(1, 2).reshape(B, N, self.dim)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):

    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        eps=1e-6,
    ):
        super().__init__()
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.norm1 = norm_layer(dim, eps=eps)
        self.mixer = Attention(
            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 > 0.0 else Identity()
        self.norm2 = norm_layer(dim, eps=eps)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

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


class ConvBlock(nn.Module):

    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        eps=1e-6,
    ):
        super().__init__()
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.norm1 = norm_layer(dim, eps=eps)
        self.mixer = nn.Conv2d(dim, dim, 5, 1, 2, groups=num_heads)
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
        self.norm2 = norm_layer(dim, eps=eps)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

    def forward(self, x):
        C, H, W = x.shape[1:]
        x = x + self.drop_path(self.mixer(x))
        x = self.norm1(x.flatten(2).transpose(1, 2))
        x = self.norm2(x + self.drop_path(self.mlp(x)))
        x = x.transpose(1, 2).reshape(-1, C, H, W)
        return x


class FlattenTranspose(nn.Module):

    def forward(self, x):
        return x.flatten(2).transpose(1, 2)


class SubSample2D(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        stride=[2, 1],
    ):
        super().__init__()
        self.conv = nn.Conv2d(in_channels,
                              out_channels,
                              kernel_size=3,
                              stride=stride,
                              padding=1)
        self.norm = nn.LayerNorm(out_channels)

    def forward(self, x, sz):
        # print(x.shape)
        x = self.conv(x)
        C, H, W = x.shape[1:]
        x = self.norm(x.flatten(2).transpose(1, 2))
        x = x.transpose(1, 2).reshape(-1, C, H, W)
        return x, [H, W]


class SubSample1D(nn.Module):

    def __init__(
        self,
        in_channels,
        out_channels,
        stride=[2, 1],
    ):
        super().__init__()
        self.conv = nn.Conv2d(in_channels,
                              out_channels,
                              kernel_size=3,
                              stride=stride,
                              padding=1)
        self.norm = nn.LayerNorm(out_channels)

    def forward(self, x, sz):
        C = x.shape[-1]
        x = x.transpose(1, 2).reshape(-1, C, sz[0], sz[1])
        x = self.conv(x)
        C, H, W = x.shape[1:]
        x = self.norm(x.flatten(2).transpose(1, 2))
        return x, [H, W]


class IdentitySize(nn.Module):

    def forward(self, x, sz):
        return x, sz


class SVTRStage(nn.Module):

    def __init__(self,
                 dim=64,
                 out_dim=256,
                 depth=3,
                 mixer=['Local'] * 3,
                 sub_k=[2, 1],
                 num_heads=2,
                 mlp_ratio=4,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.0,
                 attn_drop_rate=0.0,
                 drop_path=[0.1] * 3,
                 norm_layer=nn.LayerNorm,
                 act=nn.GELU,
                 eps=1e-6,
                 downsample=None,
                 **kwargs):
        super().__init__()
        self.dim = dim

        conv_block_num = sum([1 if mix == 'Conv' else 0 for mix in mixer])
        self.blocks = nn.Sequential()
        for i in range(depth):
            if mixer[i] == 'Conv':
                self.blocks.append(
                    ConvBlock(
                        dim=dim,
                        num_heads=num_heads,
                        mlp_ratio=mlp_ratio,
                        drop=drop_rate,
                        act_layer=act,
                        drop_path=drop_path[i],
                        norm_layer=norm_layer,
                        eps=eps,
                    ))
            else:
                self.blocks.append(
                    Block(
                        dim=dim,
                        num_heads=num_heads,
                        mlp_ratio=mlp_ratio,
                        qkv_bias=qkv_bias,
                        qk_scale=qk_scale,
                        drop=drop_rate,
                        act_layer=act,
                        attn_drop=attn_drop_rate,
                        drop_path=drop_path[i],
                        norm_layer=norm_layer,
                        eps=eps,
                    ))
            if i == conv_block_num - 1 and mixer[-1] != 'Conv':
                self.blocks.append(FlattenTranspose())
        if downsample:
            if mixer[-1] == 'Conv':
                self.downsample = SubSample2D(dim, out_dim, stride=sub_k)
            elif mixer[-1] == 'Global':
                self.downsample = SubSample1D(dim, out_dim, stride=sub_k)
        else:
            self.downsample = IdentitySize()

    def forward(self, x, sz):
        for blk in self.blocks:
            x = blk(x)
        x, sz = self.downsample(x, sz)
        return x, sz


class ADDPosEmbed(nn.Module):

    def __init__(self, feat_max_size=[8, 32], embed_dim=768):
        super().__init__()
        pos_embed = torch.zeros(
            [1, feat_max_size[0] * feat_max_size[1], embed_dim],
            dtype=torch.float32)
        trunc_normal_(pos_embed, mean=0, std=0.02)
        self.pos_embed = nn.Parameter(
            pos_embed.transpose(1, 2).reshape(1, embed_dim, feat_max_size[0],
                                              feat_max_size[1]),
            requires_grad=True,
        )

    def forward(self, x):
        sz = x.shape[2:]
        x = x + self.pos_embed[:, :, :sz[0], :sz[1]]
        return x


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

    def __init__(
        self,
        in_channels=3,
        feat_max_size=[8, 32],
        embed_dim=768,
        use_pos_embed=False,
        flatten=False,
    ):
        super().__init__()
        self.patch_embed = nn.Sequential(
            ConvBNLayer(
                in_channels=in_channels,
                out_channels=embed_dim // 2,
                kernel_size=3,
                stride=2,
                padding=1,
                act=nn.GELU,
                bias=None,
            ),
            ConvBNLayer(
                in_channels=embed_dim // 2,
                out_channels=embed_dim,
                kernel_size=3,
                stride=2,
                padding=1,
                act=nn.GELU,
                bias=None,
            ),
        )
        if use_pos_embed:
            self.patch_embed.append(ADDPosEmbed(feat_max_size, embed_dim))
        if flatten:
            self.patch_embed.append(FlattenTranspose())

    def forward(self, x):
        sz = x.shape[2:]
        x = self.patch_embed(x)
        return x, [sz[0] // 4, sz[1] // 4]


class LastStage(nn.Module):

    def __init__(self, in_channels, out_channels, last_drop, out_char_num):
        super().__init__()
        self.last_conv = nn.Linear(
            in_channels, out_channels,
            bias=False)  # self.num_features, self.out_channels, bias=False)
        self.hardswish = nn.Hardswish()
        self.dropout = nn.Dropout(p=last_drop)

    def forward(self, x, sz):
        x = x.reshape(-1, sz[0], sz[1], x.shape[-1])
        x = x.mean(1)
        x = self.last_conv(x)
        x = self.hardswish(x)
        x = self.dropout(x)
        return x, [1, sz[1]]


class Feat2D(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x, sz):
        # b, L c
        # H W
        C = x.shape[-1]
        x = x.transpose(1, 2).reshape(-1, C, sz[0], sz[1])
        return x, sz


# class LastStage(nn.Module):
#     def __init__(self, in_channels, out_channels, last_drop, out_char_num):
#         super().__init__()
#         self.avg_pool = nn.AdaptiveAvgPool2d([1, out_char_num])
#         self.last_conv = nn.Conv2d(
#                 in_channels=in_channels,
#                 out_channels=out_channels,
#                 kernel_size=1,
#                 stride=1,
#                 padding=0,
#                 bias=False,
#             )
#         self.hardswish = nn.Hardswish()
#         self.dropout = nn.Dropout(p=last_drop)
#     def forward(self, x, sz):
#         # x = x.reshape(-1, sz[0], sz[1], x.shape[-1])
#         C = x.shape[-1]
#         x = self.avg_pool(x.transpose(1, 2).reshape(-1, C, sz[0], sz[1]))
#         x = self.last_conv(x)
#         sz = x.shape[2:]
#         x = self.hardswish(x)
#         x = self.dropout(x)
#         x = x.flatten(2).transpose(1, 2)
#         return x, sz


class SVTRv2LNConv(nn.Module):

    def __init__(self,
                 max_sz=[32, 128],
                 in_channels=3,
                 out_channels=192,
                 out_char_num=25,
                 depths=[3, 6, 3],
                 dims=[64, 128, 256],
                 mixer=[['Conv'] * 3, ['Conv'] * 3 + ['Global'] * 3,
                        ['Global'] * 3],
                 use_pos_embed=True,
                 sub_k=[[1, 1], [2, 1], [1, 1]],
                 num_heads=[2, 4, 8],
                 mlp_ratio=4,
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.0,
                 last_drop=0.1,
                 attn_drop_rate=0.0,
                 drop_path_rate=0.1,
                 norm_layer=nn.LayerNorm,
                 act=nn.GELU,
                 last_stage=False,
                 feat2d=False,
                 eps=1e-6,
                 **kwargs):
        super().__init__()
        num_stages = len(depths)
        self.num_features = dims[-1]

        feat_max_size = [max_sz[0] // 4, max_sz[1] // 4]
        self.pope = POPatchEmbed(
            in_channels=in_channels,
            feat_max_size=feat_max_size,
            embed_dim=dims[0],
            use_pos_embed=use_pos_embed,
            flatten=mixer[0][0] != 'Conv',
        )

        dpr = np.linspace(0, drop_path_rate,
                          sum(depths))  # stochastic depth decay rule

        self.stages = nn.ModuleList()
        for i_stage in range(num_stages):
            stage = SVTRStage(
                dim=dims[i_stage],
                out_dim=dims[i_stage + 1] if i_stage < num_stages - 1 else 0,
                depth=depths[i_stage],
                mixer=mixer[i_stage],
                sub_k=sub_k[i_stage],
                num_heads=num_heads[i_stage],
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_stage]):sum(depths[:i_stage + 1])],
                norm_layer=norm_layer,
                act=act,
                downsample=False if i_stage == num_stages - 1 else True,
                eps=eps,
            )
            self.stages.append(stage)

        self.out_channels = self.num_features
        self.last_stage = last_stage
        if last_stage:
            self.out_channels = out_channels
            self.stages.append(
                LastStage(self.num_features, out_channels, last_drop,
                          out_char_num))
        if feat2d:
            self.stages.append(Feat2D())
        self.apply(self._init_weights)

    def _init_weights(self, m: nn.Module):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, mean=0, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                zeros_(m.bias)
        if isinstance(m, nn.LayerNorm):
            zeros_(m.bias)
            ones_(m.weight)
        if isinstance(m, nn.Conv2d):
            kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'patch_embed', 'downsample', 'pos_embed'}

    def forward(self, x):
        x, sz = self.pope(x)
        for stage in self.stages:
            x, sz = stage(x, sz)
        return x