refactor: fix constraint, re-use get_weight

        return None

# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py

# adapted from kohya's implementation 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):

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

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

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

        self.num_blocks = self.dim

        elif "Conv" in self.sd_module.__class__.__name__:

            self.out_dim = self.sd_module.out_channels

        self.constraint = self.alpha

        #self.constraint = self.alpha * self.out_dim

        self.constraint = self.alpha * self.out_dim

        self.block_size = self.out_dim // self.num_blocks

        self.org_module: list[torch.Module] = [self.sd_module]

        self.R = self.get_weight()

        self.R = self.get_weight(self.oft_blocks)

        self.apply_to()

    # replace forward method of original linear rather than replacing the module

    # how do we revert this to unload the weights?

    def apply_to(self):

        self.org_forward = self.org_module[0].forward

        self.org_module[0].forward = self.forward

    def get_weight(self, multiplier=None):

        if not multiplier:

            multiplier = self.multiplier()

        block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)

    def get_weight(self, oft_blocks, multiplier=None):

        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=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 = multiplier * block_R + (1 - multiplier) * I

        R = torch.block_diag(*block_R_weighted)

        #block_R_weighted = multiplier * block_R + (1 - multiplier) * I

        #R = torch.block_diag(*block_R_weighted)

        R = torch.block_diag(*block_R)

        return R

    def calc_updown(self, orig_weight):

        # this works

        # R = self.R

        self.R = self.get_weight(self.multiplier())

        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)

        # sending R to device causes major deepfrying i.e. just doesn't work

        # R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)

        R = self.get_weight(oft_blocks)

        self.R = R

        # if orig_weight.dim() == 4:

        #     weight = torch.einsum("oihw, op -> pihw", orig_weight, R)

        # else:

        #     weight = torch.einsum("oi, op -> pi", orig_weight, R)

        updown = orig_weight @ self.R

        updown = orig_weight @ R

        output_shape = self.oft_blocks.shape

        ## this works

        # updown = orig_weight @ R

        # output_shape = [orig_weight.size(0), R.size(1)]

        return self.finalize_updown(updown, orig_weight, output_shape)

    def forward(self, x, y=None):

        x = self.org_forward(x)

        if self.multiplier() == 0.0:

            return x

        # calculating R here is excruciatingly slow

        #R = self.get_weight().to(x.device, dtype=x.dtype)

        R = self.R.to(x.device, dtype=x.dtype)

        if x.dim() == 4:

            x = x.permute(0, 2, 3, 1)

            x = torch.matmul(x, R)