src/peft/tuners/hra/layer.py [205:244]:
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    def get_delta_weight(self, adapter_name: str, reverse: bool = False) -> torch.Tensor:
        rank = self.hra_r[adapter_name]
        apply_GS = self.hra_apply_GS[adapter_name]
        opt_u = self.hra_u[adapter_name]
        shape = opt_u.shape

        if apply_GS:
            weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
            for i in range(1, rank):
                ui = opt_u[:, i].view(-1, 1)
                for j in range(i):
                    ui = ui - (weight[j].t() @ ui) * weight[j]
                weight.append((ui / ui.norm()).view(-1, 1))
            weight = torch.cat(weight, dim=1)
            weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()

        else:
            opt_u = opt_u / opt_u.norm(dim=0)
            weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
            if reverse:
                indices = range(rank - 1, -1, -1)
            else:
                indices = range(rank)

            for i in indices:
                ui = opt_u[:, i].view(-1, 1)
                weight = weight - 2 * weight @ ui @ ui.t()

        return weight

    def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
        previous_dtype = x.dtype

        if self.disable_adapters:
            if self.merged:
                self.unmerge()
            result = self.base_layer(x, *args, **kwargs)
        elif self.merged:
            result = self.base_layer(x, *args, **kwargs)
        else:
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src/peft/tuners/hra/layer.py [377:416]:
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    def get_delta_weight(self, adapter_name: str, reverse: bool = False) -> torch.Tensor:
        rank = self.hra_r[adapter_name]
        apply_GS = self.hra_apply_GS[adapter_name]
        opt_u = self.hra_u[adapter_name]
        shape = opt_u.shape

        if apply_GS:
            weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)]
            for i in range(1, rank):
                ui = opt_u[:, i].view(-1, 1)
                for j in range(i):
                    ui = ui - (weight[j].t() @ ui) * weight[j]
                weight.append((ui / ui.norm()).view(-1, 1))
            weight = torch.cat(weight, dim=1)
            weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t()

        else:
            opt_u = opt_u / opt_u.norm(dim=0)
            weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype)
            if reverse:
                indices = range(rank - 1, -1, -1)
            else:
                indices = range(rank)

            for i in indices:
                ui = opt_u[:, i].view(-1, 1)
                weight = weight - 2 * weight @ ui @ ui.t()

        return weight

    def forward(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
        previous_dtype = x.dtype

        if self.disable_adapters:
            if self.merged:
                self.unmerge()
            result = self.base_layer(x, *args, **kwargs)
        elif self.merged:
            result = self.base_layer(x, *args, **kwargs)
        else:
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