## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881


import torch
import torch.nn as nn
import torch.nn.functional as F
from pdb import set_trace as stx
import numbers
from basicsr.utils.registry import ARCH_REGISTRY
from einops import rearrange
import math


##########################################################################
## Layer Norm


def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')


def to_4d(x, h, w):
    return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)


class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma + 1e-5) * self.weight


class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type == 'BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)


##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim * ffn_expansion_factor)

        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(
            hidden_features * 2, hidden_features * 2, kernel_size=3, stride=1, padding=1, groups=hidden_features * 2, bias=bias
        )

        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x


##########################################################################
## Multi-DConv Head Transposed Self-Attention (MDTA)
class Attention(nn.Module):
    def __init__(self, dim, num_heads, bias):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
        self.qkv_dwconv = nn.Conv2d(dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias)
        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        b, c, h, w = x.shape

        qkv = self.qkv_dwconv(self.qkv(x))
        q, k, v = qkv.chunk(3, dim=1)

        q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
        v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)

        q = torch.nn.functional.normalize(q, dim=-1)
        k = torch.nn.functional.normalize(k, dim=-1)

        attn = (q @ k.transpose(-2, -1)) * self.temperature
        attn = attn.softmax(dim=-1)

        out = attn @ v

        out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)

        out = self.project_out(out)
        return out


##########################################################################
class TransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(TransformerBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = Attention(dim, num_heads, bias)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x


##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)

        return x


##########################################################################
## Resizing modules
class Downsample(nn.Module):
    def __init__(self, n_feat):
        super(Downsample, self).__init__()

        self.body = nn.Sequential(
            nn.Conv2d(n_feat, n_feat // 2, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelUnshuffle(2)
        )

    def forward(self, x):
        return self.body(x)


class Upsample(nn.Module):
    def __init__(self, n_feat):
        super(Upsample, self).__init__()

        self.body = nn.Sequential(
            nn.Conv2d(n_feat, n_feat * 2, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(2)
        )

    def forward(self, x):
        return self.body(x)


class SR_Upsample(nn.Sequential):
    """SR_Upsample module.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of features.
    """

    def __init__(self, scale, num_feat):
        m = []

        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, kernel_size=3, stride=1, padding=1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
        super(SR_Upsample, self).__init__(*m)


##########################################################################
##---------- Restormer -----------------------
@ARCH_REGISTRY.register()
class Restormer_Base(nn.Module):
    def __init__(
        self,
        inp_channels=3,
        out_channels=3,
        dim=48,
        num_blocks=[4, 6, 6, 8],
        num_refinement_blocks=4,
        heads=[1, 2, 4, 8],
        ffn_expansion_factor=2.66,
        bias=False,
        LayerNorm_type='WithBias',  ## Other option 'BiasFree'
        dual_pixel_task=False,  ## True for dual-pixel defocus deblurring only. Also set inp_channels=6
        scale=1,
        pre_upsample=True,
    ):

        super(Restormer_Base, self).__init__()
        print("Initializing Restormer_Base")
        self.scale = scale

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)
        self.encoder_level1 = nn.Sequential(
            *[
                TransformerBlock(
                    dim=dim,
                    num_heads=heads[0],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[0])
            ]
        )

        self.down1_2 = Downsample(dim)  ## From Level 1 to Level 2
        self.encoder_level2 = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**1),
                    num_heads=heads[1],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[1])
            ]
        )

        self.down2_3 = Downsample(int(dim * 2**1))  ## From Level 2 to Level 3
        self.encoder_level3 = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**2),
                    num_heads=heads[2],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[2])
            ]
        )

        self.down3_4 = Downsample(int(dim * 2**2))  ## From Level 3 to Level 4
        self.latent = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**3),
                    num_heads=heads[3],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[3])
            ]
        )

        self.up4_3 = Upsample(int(dim * 2**3))  ## From Level 4 to Level 3
        self.reduce_chan_level3 = nn.Conv2d(int(dim * 2**3), int(dim * 2**2), kernel_size=1, bias=bias)
        self.decoder_level3 = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**2),
                    num_heads=heads[2],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[2])
            ]
        )

        self.up3_2 = Upsample(int(dim * 2**2))  ## From Level 3 to Level 2
        self.reduce_chan_level2 = nn.Conv2d(int(dim * 2**2), int(dim * 2**1), kernel_size=1, bias=bias)
        self.decoder_level2 = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**1),
                    num_heads=heads[1],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[1])
            ]
        )

        self.up2_1 = Upsample(int(dim * 2**1))  ## From Level 2 to Level 1  (NO 1x1 conv to reduce channels)

        self.decoder_level1 = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**1),
                    num_heads=heads[0],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_blocks[0])
            ]
        )

        self.refinement = nn.Sequential(
            *[
                TransformerBlock(
                    dim=int(dim * 2**1),
                    num_heads=heads[0],
                    ffn_expansion_factor=ffn_expansion_factor,
                    bias=bias,
                    LayerNorm_type=LayerNorm_type,
                )
                for i in range(num_refinement_blocks)
            ]
        )

        #### For Dual-Pixel Defocus Deblurring Task ####
        self.dual_pixel_task = dual_pixel_task
        if self.dual_pixel_task:
            self.skip_conv = nn.Conv2d(dim, int(dim * 2**1), kernel_size=1, bias=bias)
        ###########################

        self.output = nn.Conv2d(int(dim * 2**1), out_channels, kernel_size=3, stride=1, padding=1, bias=bias)

    def forward(self, inp_img):
        if self.scale > 1:
            inp_img = F.interpolate(inp_img, scale_factor=self.scale, mode='bilinear', align_corners=False)
        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)

        inp_enc_level2 = self.down1_2(out_enc_level1)
        out_enc_level2 = self.encoder_level2(inp_enc_level2)

        inp_enc_level3 = self.down2_3(out_enc_level2)
        out_enc_level3 = self.encoder_level3(inp_enc_level3)

        inp_enc_level4 = self.down3_4(out_enc_level3)
        latent = self.latent(inp_enc_level4)

        inp_dec_level3 = self.up4_3(latent)
        inp_dec_level3 = torch.cat([inp_dec_level3, out_enc_level3], 1)
        inp_dec_level3 = self.reduce_chan_level3(inp_dec_level3)
        out_dec_level3 = self.decoder_level3(inp_dec_level3)

        inp_dec_level2 = self.up3_2(out_dec_level3)
        inp_dec_level2 = torch.cat([inp_dec_level2, out_enc_level2], 1)
        inp_dec_level2 = self.reduce_chan_level2(inp_dec_level2)
        out_dec_level2 = self.decoder_level2(inp_dec_level2)

        inp_dec_level1 = self.up2_1(out_dec_level2)
        inp_dec_level1 = torch.cat([inp_dec_level1, out_enc_level1], 1)
        out_dec_level1 = self.decoder_level1(inp_dec_level1)

        out_dec_level1 = self.refinement(out_dec_level1)

        #### For Dual-Pixel Defocus Deblurring Task ####
        if self.dual_pixel_task:
            out_dec_level1 = out_dec_level1 + self.skip_conv(inp_enc_level1)
            out_dec_level1 = self.output(out_dec_level1)
        ###########################
        out_dec_level1 = self.output(out_dec_level1) + inp_img

        return out_dec_level1


if __name__ == "__main__":
    model = Restormer_Base(
        inp_channels=3,
        out_channels=3,
        dim=48,
        num_blocks=[4, 6, 6, 8],
        num_refinement_blocks=4,
        heads=[1, 2, 4, 8],
        ffn_expansion_factor=2.66,
        bias=False,
        LayerNorm_type='WithBias',  ## Other option 'BiasFree'
        dual_pixel_task=False,  ## True for dual-pixel defocus deblurring only. Also set inp_channels=6
        scale=4,
        pre_upsample=False,
    )

    x = torch.randn((1, 3, 64, 64))
    x = model(x)
    print(x.shape)
