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


# Layer Norm
class LayerNorm(nn.Module):
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_first"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError
        self.normalized_shape = (normalized_shape, )

    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x

# CCM
class CCM(nn.Module):
    def __init__(self, dim, growth_rate=2.0):
        super().__init__()
        hidden_dim = int(dim * growth_rate)

        self.ccm = nn.Sequential(
            nn.Conv2d(dim, hidden_dim, 3, 1, 1),
            nn.GELU(), 
            nn.Conv2d(hidden_dim, dim, 1, 1, 0)
        )

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


# SAFM
class SAFM(nn.Module):
    def __init__(self, dim, n_levels=4):
        super().__init__()
        self.n_levels = n_levels
        chunk_dim = dim // n_levels

        # Spatial Weighting
        self.mfr = nn.ModuleList([nn.Conv2d(chunk_dim, chunk_dim, 3, 1, 1, groups=chunk_dim) for i in range(self.n_levels)])
        
        # # Feature Aggregation
        self.aggr = nn.Conv2d(dim, dim, 1, 1, 0)
        
        # Activation
        self.act = nn.GELU() 

    def forward(self, x):
        h, w = x.size()[-2:]

        xc = x.chunk(self.n_levels, dim=1)
        out = []
        for i in range(self.n_levels):
            if i > 0:
                p_size = (h//2**i, w//2**i)
                s = F.adaptive_max_pool2d(xc[i], p_size)
                s = self.mfr[i](s)
                s = F.interpolate(s, size=(h, w), mode='nearest')
            else:
                s = self.mfr[i](xc[i])
            out.append(s)

        out = self.aggr(torch.cat(out, dim=1))
        out = self.act(out) * x
        return out

class AttBlock(nn.Module):
    def __init__(self, dim, ffn_scale=2.0):
        super().__init__()

        self.norm1 = LayerNorm(dim) 
        self.norm2 = LayerNorm(dim) 

        # Multiscale Block
        self.safm = SAFM(dim) 
        # Feedforward layer
        self.ccm = CCM(dim, ffn_scale) 

    def forward(self, x):
        x = self.safm(self.norm1(x)) + x
        x = self.ccm(self.norm2(x)) + x
        return x
        
        
class SAFMN(nn.Module):
    def __init__(self, dim, n_blocks=8, ffn_scale=2.0, upscaling_factor=4):
        super().__init__()
        self.to_feat = nn.Conv2d(3, dim, 3, 1, 1)

        self.feats = nn.Sequential(*[AttBlock(dim, ffn_scale) for _ in range(n_blocks)])

        self.to_img = nn.Sequential(
            nn.Conv2d(dim, 3 * upscaling_factor**2, 3, 1, 1),
            nn.PixelShuffle(upscaling_factor)
        )

    def forward(self, x):
        x = self.to_feat(x)
        x = self.feats(x) + x
        x = self.to_img(x)
        return x