import torch
import network


class ModuleTypeOFT(network.ModuleType):
    def create_module(self, net: network.Network, weights: network.NetworkWeights):
        if all(x in weights.w for x in ["oft_blocks"]):
            return NetworkModuleOFT(net, weights)

        return None

# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
class NetworkModuleOFT(network.NetworkModule):
    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
        super().__init__(net, weights)

        self.oft_blocks = weights.w["oft_blocks"]
        self.alpha = weights.w["alpha"]

        self.dim = self.oft_blocks.shape[0]
        self.num_blocks = self.dim

        #if type(self.alpha) == torch.Tensor:
        #    self.alpha = self.alpha.detach().numpy()

        if "Linear" in self.sd_module.__class__.__name__:
            self.out_dim = self.sd_module.out_features
        elif "Conv" in self.sd_module.__class__.__name__:
            self.out_dim = self.sd_module.out_channels

        self.constraint = self.alpha * self.out_dim
        self.block_size = self.out_dim // self.num_blocks

        self.oft_multiplier = self.multiplier()

        # replace forward method of original linear rather than replacing the module
        # self.org_forward = self.sd_module.forward
        # self.sd_module.forward = self.forward
    
    def get_weight(self):
        block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
        norm_Q = torch.norm(block_Q.flatten())
        new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
        block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
        I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
        block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())

        block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
        R = torch.block_diag(*block_R_weighted)

        return R

    def calc_updown(self, orig_weight):
        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
        block_Q = oft_blocks - oft_blocks.transpose(1, 2)
        norm_Q = torch.norm(block_Q.flatten())
        new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
        block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
        I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
        block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())

        block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
        R = torch.block_diag(*block_R_weighted)
        #R = self.get_weight().to(orig_weight.device, dtype=orig_weight.dtype)
        # W = R*W_0
        updown = orig_weight + R
        output_shape = [R.size(0), orig_weight.size(1)]
        return self.finalize_updown(updown, orig_weight, output_shape)
    
    # def forward(self, x, y=None):
    #     x = self.org_forward(x)
    #     if self.oft_multiplier == 0.0:
    #         return x

    #     R = self.get_weight().to(x.device, dtype=x.dtype)
    #     if x.dim() == 4:
    #         x = x.permute(0, 2, 3, 1)
    #         x = torch.matmul(x, R)
    #         x = x.permute(0, 3, 1, 2)
    #     else:
    #         x = torch.matmul(x, R)
    #     return x