style: cleanup oft

import torch

import network

from modules import devices

class ModuleTypeOFT(network.ModuleType):

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

        self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)

        #self.org_weight = self.org_module[0].weight.to(devices.cpu, copy=True)

        init_multiplier = self.multiplier() * self.calc_scale()

        self.last_multiplier = init_multiplier

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

        self.merged_weight = self.merge_weight()

        self.apply_to()

        self.merged = False

        # weights_backup = getattr(self.org_module[0], 'network_weights_backup', None)

        # if weights_backup is None:

        #     self.org_module[0].network_weights_backup = self.org_weight

    def merge_weight(self):

        #org_sd = self.org_module[0].state_dict()

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

        if self.org_weight.dim() == 4:

            weight = torch.einsum("oihw, op -> pihw", self.org_weight, R)

        else:

            weight = torch.einsum("oi, op -> pi", self.org_weight, R)

        #org_sd['weight'] = weight

        # replace weight

        #self.org_module[0].load_state_dict(org_sd)

        return weight

        pass

    def replace_weight(self, new_weight):

        org_sd = self.org_module[0].state_dict()

        self.org_module[0].load_state_dict(org_sd)

        self.merged = False

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

    # how do we revert this to unload the weights?

    # FIXME: hook forward method of original linear, but how do we undo the hook when we are done?

    def apply_to(self):

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

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

        block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())

        block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I

        R = torch.block_diag(*block_R_weighted)

        #R = torch.block_diag(*block_R)

        return R

    def calc_updown(self, orig_weight):

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

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

        ##self.R = R

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

        ##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 @ R

        #updown = weight

        updown = torch.zeros_like(orig_weight, device=orig_weight.device, dtype=orig_weight.dtype)

        #updown = orig_weight

        output_shape = orig_weight.shape

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

        #output_shape = self.oft_blocks.shape

    def pre_forward_hook(self, module, input):

        multiplier = self.multiplier() * self.calc_scale()

        if not multiplier==self.last_multiplier or not self.merged:

        #if multiplier != self.last_multiplier or not self.merged:

        if not multiplier==self.last_multiplier or not self.merged:

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

            self.last_multiplier = multiplier

            self.merged_weight = self.merge_weight()

            self.replace_weight(self.merged_weight)

        #elif not self.merged:

        #    self.replace_weight(self.merged_weight)

    def forward_hook(self, module, args, output):

        pass

        #output = output * self.multiplier() * self.calc_scale()

        #if len(args) > 0:

        #    y = args[0]

        #    output = output + y

        #return output

        #if self.merged:

        #    pass

            #self.restore_weight()

        #print(f'Forward hook in {self.network_key} called')

        #x = output

        #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)

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

        #else:

        #    x = torch.matmul(x, R)

        #return x

    # 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)

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

    #     else:

    #         x = torch.matmul(x, R)

    #     return x