src/peft/tuners/lora/bnb.py [302:346]:
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            super().__init__()
            LoraLayer.__init__(self, base_layer)
            self.fan_in_fan_out = False

            self._active_adapter = adapter_name
            self.update_layer(
                adapter_name,
                r,
                lora_alpha=lora_alpha,
                lora_dropout=lora_dropout,
                init_lora_weights=init_lora_weights,
                use_rslora=use_rslora,
                use_dora=use_dora,
                lora_bias=lora_bias,
            )

        def resolve_lora_variant(self, *, use_dora: bool, **kwargs) -> Optional[LoraVariant]:
            if not use_dora:
                return None

            from .variants import DoraLinearVariant

            return DoraLinearVariant()

        def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
            """
            Merge the active adapter weights into the base weights

            Args:
                safe_merge (`bool`, *optional*):
                    If True, the merge operation will be performed in a copy of the original weights and check for NaNs
                    before merging the weights. This is useful if you want to check if the merge operation will produce
                    NaNs. Defaults to `False`.
                adapter_names (`list[str]`, *optional*):
                    The list of adapter names that should be merged. If None, all active adapters will be merged.
                    Defaults to `None`.
            """
            adapter_names = check_adapters_to_merge(self, adapter_names)
            if not adapter_names:
                # no adapter to merge
                return

            for active_adapter in adapter_names:
                if active_adapter not in self.lora_A.keys():
                    continue
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src/peft/tuners/lora/hqq.py [50:94]:
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            super().__init__()
            LoraLayer.__init__(self, base_layer)
            self.fan_in_fan_out = False

            self._active_adapter = adapter_name
            self.update_layer(
                adapter_name,
                r,
                lora_alpha=lora_alpha,
                lora_dropout=lora_dropout,
                init_lora_weights=init_lora_weights,
                use_rslora=use_rslora,
                use_dora=use_dora,
                lora_bias=lora_bias,
            )

        def resolve_lora_variant(self, *, use_dora: bool, **kwargs) -> Optional[LoraVariant]:
            if not use_dora:
                return None

            from .variants import DoraLinearVariant

            return DoraLinearVariant()

        def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = None) -> None:
            """
            Merge the active adapter weights into the base weights

            Args:
                safe_merge (`bool`, *optional*):
                    If True, the merge operation will be performed in a copy of the original weights and check for NaNs
                    before merging the weights. This is useful if you want to check if the merge operation will produce
                    NaNs. Defaults to `False`.
                adapter_names (`list[str]`, *optional*):
                    The list of adapter names that should be merged. If None, all active adapters will be merged.
                    Defaults to `None`.
            """
            adapter_names = check_adapters_to_merge(self, adapter_names)
            if not adapter_names:
                # no adapter to merge
                return

            for active_adapter in adapter_names:
                if active_adapter not in self.lora_A.keys():
                    continue
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