# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import collections
import inspect
import os
import warnings
from contextlib import contextmanager
from copy import deepcopy
from typing import Any, Optional, Union

import packaging.version
import torch
import transformers
from accelerate import dispatch_model, infer_auto_device_map
from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules
from accelerate.utils import get_balanced_memory, named_module_tensors
from huggingface_hub import ModelCard, ModelCardData, hf_hub_download
from safetensors import safe_open
from safetensors.torch import save_file as safe_save_file
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PreTrainedModel
from transformers.modeling_outputs import QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from transformers.utils import PushToHubMixin

from . import __version__
from .config import PeftConfig
from .tuners import (
    AdaLoraModel,
    AdaptionPromptModel,
    BOFTModel,
    IA3Model,
    LoHaModel,
    LoKrModel,
    LoraModel,
    MultitaskPromptEmbedding,
    OFTModel,
    PolyModel,
    PrefixEncoder,
    PromptEmbedding,
    PromptEncoder,
    VeraModel,
    VBLoRAModel
)
from .tuners.tuners_utils import BaseTunerLayer
from .utils import (
    SAFETENSORS_WEIGHTS_NAME,
    TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
    WEIGHTS_NAME,
    PeftType,
    TaskType,
    _get_batch_size,
    _prepare_prompt_learning_config,
    _set_adapter,
    _set_trainable,
    get_peft_model_state_dict,
    id_tensor_storage,
    infer_device,
    load_peft_weights,
    set_peft_model_state_dict,
    shift_tokens_right,
)


PEFT_TYPE_TO_MODEL_MAPPING = {
    PeftType.LORA: LoraModel,
    PeftType.LOHA: LoHaModel,
    PeftType.LOKR: LoKrModel,
    PeftType.PROMPT_TUNING: PromptEmbedding,
    PeftType.P_TUNING: PromptEncoder,
    PeftType.PREFIX_TUNING: PrefixEncoder,
    PeftType.ADALORA: AdaLoraModel,
    PeftType.BOFT: BOFTModel,
    PeftType.ADAPTION_PROMPT: AdaptionPromptModel,
    PeftType.IA3: IA3Model,
    PeftType.OFT: OFTModel,
    PeftType.POLY: PolyModel,
    PeftType.VERA: VeraModel,
    PeftType.VBLORA: VBLoRAModel,
}


class PeftModel(PushToHubMixin, torch.nn.Module):
    """
    Base model encompassing various Peft methods.

    Args:
        model ([`~transformers.PreTrainedModel`]): The base transformer model used for Peft.
        peft_config ([`PeftConfig`]): The configuration of the Peft model.
        adapter_name (`str`,  *optional*): The name of the adapter, defaults to `"default"`.

    **Attributes**:
        - **base_model** ([`torch.nn.Module`]) -- The base transformer model used for Peft.
        - **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model.
        - **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when
            saving the model.
        - **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if
            using [`PromptLearningConfig`].
        - **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if
            using [`PromptLearningConfig`].
        - **transformer_backbone_name** (`str`) -- The name of the transformer
            backbone in the base model if using [`PromptLearningConfig`].
        - **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone
            in the base model if using [`PromptLearningConfig`].
    """

    def __init__(self, model: PreTrainedModel, peft_config: PeftConfig, adapter_name: str = "default") -> None:
        super().__init__()
        self.modules_to_save = None
        self.active_adapter = adapter_name
        self.peft_type = peft_config.peft_type
        # These args are special PEFT arguments that users can pass. They need to be removed before passing them to
        # forward.
        self.special_peft_forward_args = {"adapter_names"}

        self._is_prompt_learning = peft_config.is_prompt_learning
        if self._is_prompt_learning:
            self._peft_config = {adapter_name: peft_config}
            self.base_model = model
            self.add_adapter(adapter_name, peft_config)
        else:
            self._peft_config = None
            cls = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type]
            self.base_model = cls(model, {adapter_name: peft_config}, adapter_name)
            self.set_additional_trainable_modules(peft_config, adapter_name)

        if getattr(model, "is_gradient_checkpointing", True):
            model = self._prepare_model_for_gradient_checkpointing(model)

        # the `pretraining_tp` is set for some models to simulate Tensor Parallelism during inference to avoid
        # numerical differences, https://github.com/pytorch/pytorch/issues/76232 - to avoid any unexpected
        # behavior we disable that in this line.
        if hasattr(self.base_model, "config") and hasattr(self.base_model.config, "pretraining_tp"):
            self.base_model.config.pretraining_tp = 1

    @property
    def peft_config(self) -> dict[str, PeftConfig]:
        if self._is_prompt_learning:
            return self._peft_config
        return self.base_model.peft_config

    @property
    def active_adapters(self) -> list[str]:
        try:
            adapters = self.base_model.active_adapters
        except AttributeError:
            adapters = self.active_adapter
            if isinstance(adapters, str):
                adapters = [adapters]
        return adapters

    @peft_config.setter
    def peft_config(self, value: dict[str, PeftConfig]):
        if self._is_prompt_learning:
            self._peft_config = value
        else:
            self.base_model.peft_config = value

    def save_pretrained(
        self,
        save_directory: str,
        safe_serialization: bool = True,
        selected_adapters: Optional[list[str]] = None,
        save_embedding_layers: Union[str, bool] = "auto",
        is_main_process: bool = True,
        **kwargs: Any,
    ) -> None:
        r"""
        This function saves the adapter model and the adapter configuration files to a directory, so that it can be
        reloaded using the [`PeftModel.from_pretrained`] class method, and also used by the [`PeftModel.push_to_hub`]
        method.

        Args:
            save_directory (`str`):
                Directory where the adapter model and configuration files will be saved (will be created if it does not
                exist).
            safe_serialization (`bool`, *optional*):
                Whether to save the adapter files in safetensors format, defaults to `True`.
            selected_adapters (`List[str]`,  *optional*):
                A list of adapters to be saved. If `None`, will default to all adapters.
            save_embedding_layers (`Union[bool, str]`, *optional*, defaults to `"auto"`):
                If `True`, save the embedding layers in addition to adapter weights. If `auto`, checks the common
                embedding layers `peft.utils.other.EMBEDDING_LAYER_NAMES` in config's `target_modules` when available.
                and automatically sets the boolean flag. This only works for 🤗 transformers models.
            is_main_process (`bool`, *optional*):
                Whether the process calling this is the main process or not. Will default to `True`. Will not save the
                checkpoint if not on the main process, which is important for multi device setups (e.g. DDP).
            kwargs (additional keyword arguments, *optional*):
                Additional keyword arguments passed along to the `push_to_hub` method.
        """
        if os.path.isfile(save_directory):
            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")

        if selected_adapters is None:
            selected_adapters = list(self.peft_config.keys())
        else:
            if any(
                selected_adapter_name not in list(self.peft_config.keys())
                for selected_adapter_name in selected_adapters
            ):
                raise ValueError(
                    f"You passed an invalid `selected_adapters` arguments, current supported adapter names are"
                    f" {list(self.peft_config.keys())} - got {selected_adapters}."
                )

        if is_main_process:
            os.makedirs(save_directory, exist_ok=True)
            self.create_or_update_model_card(save_directory)

        for adapter_name in selected_adapters:
            peft_config = self.peft_config[adapter_name]
            # save only the trainable weights
            output_state_dict = get_peft_model_state_dict(
                self,
                state_dict=kwargs.get("state_dict", None),
                adapter_name=adapter_name,
                save_embedding_layers=save_embedding_layers,
            )
            output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory
            os.makedirs(output_dir, exist_ok=True)

            if is_main_process and safe_serialization:
                # Section copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L2111-L2134
                # Safetensors does not allow tensor aliasing.
                # We're going to remove aliases before saving
                ptrs = collections.defaultdict(list)
                for name, tensor in output_state_dict.items():
                    # Sometimes in the state_dict we have non-tensor objects.
                    # e.g. in bitsandbytes we have some `str` objects in the state_dict
                    if isinstance(tensor, torch.Tensor):
                        ptrs[id_tensor_storage(tensor)].append(name)
                    else:
                        # In the non-tensor case, fall back to the pointer of the object itself
                        ptrs[id(tensor)].append(name)

                # These are all the pointers of shared tensors.
                shared_ptrs = {ptr: names for ptr, names in ptrs.items() if len(names) > 1}

                for _, names in shared_ptrs.items():
                    # Here we just clone the shared tensors to avoid tensor aliasing which is
                    # not supported in safetensors.
                    for shared_tensor_name in names[1:]:
                        output_state_dict[shared_tensor_name] = output_state_dict[shared_tensor_name].clone()

                safe_save_file(
                    output_state_dict,
                    os.path.join(output_dir, SAFETENSORS_WEIGHTS_NAME),
                    metadata={"format": "pt"},
                )
            elif is_main_process:
                torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))

            # save the config and change the inference mode to `True`
            if peft_config.base_model_name_or_path is None:
                peft_config.base_model_name_or_path = (
                    self.base_model.__dict__.get("name_or_path", None)
                    if peft_config.is_prompt_learning
                    else self.base_model.model.__dict__.get("name_or_path", None)
                )
            inference_mode = peft_config.inference_mode
            peft_config.inference_mode = True

            if peft_config.task_type is None:
                # deal with auto mapping
                base_model_class = self._get_base_model_class(
                    is_prompt_tuning=peft_config.is_prompt_learning,
                )
                parent_library = base_model_class.__module__

                auto_mapping_dict = {
                    "base_model_class": base_model_class.__name__,
                    "parent_library": parent_library,
                }
            else:
                auto_mapping_dict = None

            if is_main_process:
                peft_config.save_pretrained(output_dir, auto_mapping_dict=auto_mapping_dict)
            peft_config.inference_mode = inference_mode

    @classmethod
    def from_pretrained(
        cls,
        model: torch.nn.Module,
        model_id: Union[str, os.PathLike],
        adapter_name: str = "default",
        is_trainable: bool = False,
        config: Optional[PeftConfig] = None,
        **kwargs: Any,
    ) -> PeftModel:
        r"""
        Instantiate a PEFT model from a pretrained model and loaded PEFT weights.

        Note that the passed `model` may be modified inplace.

        Args:
            model ([`torch.nn.Module`]):
                The model to be adapted. For 🤗 Transformers models, the model should be initialized with the
                [`~transformers.PreTrainedModel.from_pretrained`].
            model_id (`str` or `os.PathLike`):
                The name of the PEFT configuration to use. Can be either:
                    - A string, the `model id` of a PEFT configuration hosted inside a model repo on the Hugging Face
                      Hub.
                    - A path to a directory containing a PEFT configuration file saved using the `save_pretrained`
                      method (`./my_peft_config_directory/`).
            adapter_name (`str`, *optional*, defaults to `"default"`):
                The name of the adapter to be loaded. This is useful for loading multiple adapters.
            is_trainable (`bool`, *optional*, defaults to `False`):
                Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be
                used for inference.
            config ([`~peft.PeftConfig`], *optional*):
                The configuration object to use instead of an automatically loaded configuration. This configuration
                object is mutually exclusive with `model_id` and `kwargs`. This is useful when configuration is already
                loaded before calling `from_pretrained`.
            kwargs: (`optional`):
                Additional keyword arguments passed along to the specific PEFT configuration class.
        """
        from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING

        # load the config
        if config is None:
            config = PEFT_TYPE_TO_CONFIG_MAPPING[
                PeftConfig._get_peft_type(
                    model_id,
                    subfolder=kwargs.get("subfolder", None),
                    revision=kwargs.get("revision", None),
                    cache_dir=kwargs.get("cache_dir", None),
                    use_auth_token=kwargs.get("use_auth_token", None),
                    token=kwargs.get("token", None),
                )
            ].from_pretrained(model_id, **kwargs)
        elif isinstance(config, PeftConfig):
            config.inference_mode = not is_trainable
        else:
            raise ValueError(f"The input config must be a PeftConfig, got {config.__class__}")

        if hasattr(model, "hf_device_map"):
            weight_map = dict(named_module_tensors(model, recurse=True))

            # recreate the offload_index for disk-offloaded modules: we need to know the location in storage of each weight
            # before the offload hook is removed from the model
            disk_modules = set()
            index = None
            for name, module in model.named_modules():
                if hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "original_devices"):
                    if hasattr(module._hf_hook.weights_map, "dataset"):
                        index = module._hf_hook.weights_map.dataset.index
                    for key in module._hf_hook.original_devices.keys():
                        if module._hf_hook.original_devices[key] == torch.device("meta"):
                            disk_modules.add(str(name) + "." + str(key))

            if disk_modules and not kwargs.get("use_safetensors", True):
                raise ValueError("Disk offloading currently only supported for safetensors")

            if index:
                offload_index = {
                    p: {
                        "safetensors_file": index[p]["safetensors_file"],
                        "weight_name": p,
                        "dtype": str(weight_map[p].dtype).replace("torch.", ""),
                    }
                    for p in weight_map.keys()
                    if p in disk_modules
                }
                kwargs["offload_index"] = offload_index

        if (getattr(model, "hf_device_map", None) is not None) and len(
            set(model.hf_device_map.values()).intersection({"cpu", "disk"})
        ) > 0:
            remove_hook_from_submodules(model)

        if config.is_prompt_learning and is_trainable:
            raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
        else:
            config.inference_mode = not is_trainable

        if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
            model = cls(model, config, adapter_name)
        else:
            model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name)
        model.load_adapter(model_id, adapter_name, is_trainable=is_trainable, **kwargs)
        return model

    def _setup_prompt_encoder(self, adapter_name: str):
        config = self.peft_config[adapter_name]
        if not hasattr(self, "prompt_encoder"):
            self.prompt_encoder = torch.nn.ModuleDict({})
            self.prompt_tokens = {}
        transformer_backbone = None
        for name, module in self.base_model.named_children():
            for param in module.parameters():
                param.requires_grad = False
            if isinstance(module, PreTrainedModel):
                # Make sure to freeze Tranformers model
                if transformer_backbone is None:
                    transformer_backbone = module
                    self.transformer_backbone_name = name
        if transformer_backbone is None:
            transformer_backbone = self.base_model

        if config.num_transformer_submodules is None:
            config.num_transformer_submodules = 2 if config.task_type == TaskType.SEQ_2_SEQ_LM else 1

        for named_param, value in list(transformer_backbone.named_parameters()):
            # for ZeRO-3, the tensor is sharded across accelerators and deepspeed modifies it to a tensor with shape [0]
            # the actual unsharded shape is stored in "ds_shape" attribute
            # special handling is needed in case the model is initialized in deepspeed.zero.Init() context or HfDeepSpeedConfig
            # has been called before
            # For reference refer to issue: https://github.com/huggingface/peft/issues/996
            deepspeed_distributed_tensor_shape = getattr(value, "ds_shape", None)

            if value.shape[0] == self.base_model.config.vocab_size or (
                deepspeed_distributed_tensor_shape is not None
                and deepspeed_distributed_tensor_shape[0] == self.base_model.config.vocab_size
            ):
                self.word_embeddings = transformer_backbone.get_submodule(named_param.replace(".weight", ""))
                break

        if config.peft_type == PeftType.PROMPT_TUNING:
            prompt_encoder = PromptEmbedding(config, self.word_embeddings)
        elif config.peft_type == PeftType.MULTITASK_PROMPT_TUNING:
            prompt_encoder = MultitaskPromptEmbedding(config, self.word_embeddings)
        elif config.peft_type == PeftType.P_TUNING:
            prompt_encoder = PromptEncoder(config)
        elif config.peft_type == PeftType.PREFIX_TUNING:
            prompt_encoder = PrefixEncoder(config)
        else:
            raise ValueError("Not supported")

        prompt_encoder = prompt_encoder.to(self.device)
        self.prompt_encoder.update(torch.nn.ModuleDict({adapter_name: prompt_encoder}))
        self.prompt_tokens[adapter_name] = torch.arange(
            config.num_virtual_tokens * config.num_transformer_submodules
        ).long()

    def _prepare_model_for_gradient_checkpointing(self, model: PreTrainedModel):
        r"""
        Prepares the model for gradient checkpointing if necessary
        """
        if not (
            getattr(model, "is_loaded_in_8bit", False)
            or getattr(model, "is_loaded_in_4bit", False)
            or getattr(model, "is_quantized", False)
        ):
            if hasattr(model, "enable_input_require_grads"):
                model.enable_input_require_grads()
            elif hasattr(model, "get_input_embeddings"):

                def make_inputs_require_grad(module, input, output):
                    output.requires_grad_(True)

                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
        return model

    def get_prompt_embedding_to_save(self, adapter_name: str) -> torch.Tensor:
        """
        Returns the prompt embedding to save when saving the model. Only applicable when using a prompt learning
        method.
        """
        prompt_encoder = self.prompt_encoder[adapter_name]
        prompt_tokens = (
            self.prompt_tokens[adapter_name].unsqueeze(0).expand(1, -1).to(prompt_encoder.embedding.weight.device)
        )
        if self.peft_config[adapter_name].peft_type == PeftType.PREFIX_TUNING:
            prompt_tokens = prompt_tokens[:, : self.peft_config[adapter_name].num_virtual_tokens]

        if self.peft_config[adapter_name].peft_type == PeftType.MULTITASK_PROMPT_TUNING:
            prompt_embeddings = super(MultitaskPromptEmbedding, prompt_encoder).forward(prompt_tokens)
        else:
            prompt_embeddings = prompt_encoder(prompt_tokens)

        return prompt_embeddings[0].detach().cpu()

    def get_prompt(self, batch_size: int, task_ids: Optional[torch.Tensor] = None) -> torch.Tensor:
        """
        Returns the virtual prompts to use for Peft. Only applicable when using a prompt learning method.
        """
        peft_config = self.active_peft_config
        prompt_encoder = self.prompt_encoder[self.active_adapter]
        prompt_tokens = (
            self.prompt_tokens[self.active_adapter]
            .unsqueeze(0)
            .expand(batch_size, -1)
            .to(prompt_encoder.embedding.weight.device)
        )
        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            prompt_tokens = prompt_tokens[:, : peft_config.num_virtual_tokens]
            if peft_config.inference_mode:
                past_key_values = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
            else:
                past_key_values = prompt_encoder(prompt_tokens)
            if self.base_model_torch_dtype is not None:
                past_key_values = past_key_values.to(self.base_model_torch_dtype)
            past_key_values = past_key_values.view(
                batch_size,
                peft_config.num_virtual_tokens,
                peft_config.num_layers * 2,
                peft_config.num_attention_heads,
                peft_config.token_dim // peft_config.num_attention_heads,
            )
            if peft_config.num_transformer_submodules == 2:
                past_key_values = torch.cat([past_key_values, past_key_values], dim=2)
            past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(
                peft_config.num_transformer_submodules * 2
            )
            if TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING.get(self.config.model_type, None) is not None:
                post_process_fn = TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING[self.config.model_type]
                past_key_values = post_process_fn(past_key_values)
            return past_key_values
        else:
            if peft_config.peft_type == PeftType.MULTITASK_PROMPT_TUNING:
                prompts = prompt_encoder(prompt_tokens, task_ids)
            else:
                if peft_config.inference_mode:
                    prompts = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
                else:
                    prompts = prompt_encoder(prompt_tokens)
            return prompts

    def get_nb_trainable_parameters(self) -> tuple[int, int]:
        r"""
        Returns the number of trainable parameters and the number of all parameters in the model.
        """
        trainable_params = 0
        all_param = 0
        for _, param in self.named_parameters():
            num_params = param.numel()
            # if using DS Zero 3 and the weights are initialized empty
            if num_params == 0 and hasattr(param, "ds_numel"):
                num_params = param.ds_numel

            # Due to the design of 4bit linear layers from bitsandbytes
            # one needs to multiply the number of parameters by 2 to get
            # the correct number of parameters
            if param.__class__.__name__ == "Params4bit":
                if hasattr(param, "element_size"):
                    num_bytes = param.element_size()
                elif not hasattr(param, "quant_storage"):
                    num_bytes = 1
                else:
                    num_bytes = param.quant_storage.itemsize
                num_params = num_params * 2 * num_bytes

            all_param += num_params
            if param.requires_grad:
                trainable_params += num_params

        return trainable_params, all_param

    def print_trainable_parameters(self) -> None:
        """
        Prints the number of trainable parameters in the model.

        Note: print_trainable_parameters() uses get_nb_trainable_parameters() which is different from
        num_parameters(only_trainable=True) from huggingface/transformers. get_nb_trainable_parameters() returns
        (trainable parameters, all parameters) of the Peft Model which includes modified backbone transformer model.
        For techniques like LoRA, the backbone transformer model is modified in place with LoRA modules. However, for
        prompt tuning, the backbone transformer model is unmodified. num_parameters(only_trainable=True) returns number
        of trainable parameters of the backbone transformer model which can be different.
        """
        trainable_params, all_param = self.get_nb_trainable_parameters()

        print(
            f"trainable params: {trainable_params:,d} || all params: {all_param:,d} || trainable%: {100 * trainable_params / all_param:.4f}"
        )

    def __getattr__(self, name: str):
        """Forward missing attributes to the wrapped module."""
        try:
            return super().__getattr__(name)  # defer to nn.Module's logic
        except AttributeError:
            return getattr(self.base_model, name)

    @contextmanager
    def _enable_peft_forward_hooks(self, *args, **kwargs):
        # If the base model has a method called _enable_peft_forward_hooks, it is invoked as a context. Otherwise, this
        # runs without any changes
        if hasattr(self.base_model, "_enable_peft_forward_hooks"):
            with self.base_model._enable_peft_forward_hooks(*args, **kwargs):
                yield
            return
        else:
            # nothing to enable
            yield
            return

    def forward(self, *args: Any, **kwargs: Any):
        """
        Forward pass of the model.
        """
        with self._enable_peft_forward_hooks(*args, **kwargs):
            kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
            return self.get_base_model()(*args, **kwargs)

    def generate(self, *args, **kwargs):
        with self._enable_peft_forward_hooks(*args, **kwargs):
            kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
            return self.get_base_model().generate(*args, **kwargs)

    def _get_base_model_class(self, is_prompt_tuning=False):
        """
        Returns the base model class.
        """
        if not is_prompt_tuning:
            return self.base_model.model.__class__
        return self.base_model.__class__

    @contextmanager
    def disable_adapter(self):
        """
        Context manager that disables the adapter module. Use this to run inference on the base model.

        Example:

        ```py
        >>> with model.disable_adapter():
        ...     model(inputs)
        ```
        """
        try:
            if self.peft_config[self.active_adapter].is_prompt_learning:
                # TODO: consider replacing this patching of methods with a more robust mechanism: setting a flag and
                # letting the underlying methods deal with it, same as how LoRA does it.
                old_forward = self.forward
                self.forward = self.base_model.forward
                old_prepare_inputs_for_generation = self.prepare_inputs_for_generation
                self.prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
            else:
                self.base_model.disable_adapter_layers()
            yield
        finally:
            if self.peft_config[self.active_adapter].is_prompt_learning:
                self.forward = old_forward
                self.prepare_inputs_for_generation = old_prepare_inputs_for_generation
            else:
                self.base_model.enable_adapter_layers()

    def get_base_model(self) -> torch.nn.Module:
        """
        Returns the base model.
        """
        return (
            self.base_model
            if (self.active_peft_config.is_prompt_learning or self.peft_type == PeftType.POLY)
            else self.base_model.model
        )

    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
        """
        Add an adapter to the model based on the passed configuration.

        This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`].

        The name for the new adapter should be unique.

        The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active
        adapter.

        Args:
            adapter_name (`str`):
                The name of the adapter to be added.
            peft_config ([`PeftConfig`]):
                The configuration of the adapter to be added.
        """
        if peft_config.peft_type != self.peft_type:
            raise ValueError(
                f"Cannot combine adapters with different peft types. "
                f"Found {self.peft_type} and {peft_config.peft_type}."
            )

        try:
            if peft_config.is_prompt_learning:
                self.peft_config[adapter_name] = peft_config
                if hasattr(self.config, "to_dict"):
                    dict_config = self.config.to_dict()
                else:
                    dict_config = self.config

                peft_config = _prepare_prompt_learning_config(peft_config, dict_config)
                self._setup_prompt_encoder(adapter_name)
            elif peft_config.is_adaption_prompt:
                self.base_model.add_adapter(adapter_name, peft_config)
            else:
                self.peft_config[adapter_name] = peft_config
                self.base_model.inject_adapter(self.base_model.model, adapter_name)
        except Exception:  # something went wrong, roll back
            if adapter_name in self.peft_config:
                del self.peft_config[adapter_name]
            raise

        self.set_additional_trainable_modules(peft_config, adapter_name)

    def set_additional_trainable_modules(self, peft_config, adapter_name):
        if getattr(peft_config, "modules_to_save", None) is not None:
            if self.modules_to_save is None:
                self.modules_to_save = set(peft_config.modules_to_save)
            else:
                self.modules_to_save.update(peft_config.modules_to_save)
            _set_trainable(self, adapter_name)  # this may add a new ModulesToSaveWrapper

    @classmethod
    def _split_kwargs(cls, kwargs: dict[str, Any]):
        _kwargs_not_in_hf_hub_download_signature = ("use_auth_token",)
        hf_hub_download_kwargs = {}
        other_kwargs = {}

        for key, value in kwargs.items():
            if key in inspect.signature(hf_hub_download).parameters or key in _kwargs_not_in_hf_hub_download_signature:
                hf_hub_download_kwargs[key] = value
            else:
                other_kwargs[key] = value

        return hf_hub_download_kwargs, other_kwargs

    def _update_offload(self, offload_index: dict[str, dict[str, str]], adapters_weights: dict[str, torch.tensor]):
        """
        Update the offload_index and safetensors files for loading and mergine PeftModels with disk-offloaded modules.

        Args:
            offload_index (Dict[str: str]):
                Dictionary of disk-offloaded modules with their metadata and safetensors filenames
            adapters_weights (Dict[str: torch.tensor]):
                Dictionary of Peft adapter module names and weights
        """

        if not offload_index:
            return offload_index

        prefix = "base_model.model."
        # rename offload index weight and model names
        adapter_names = list(self.peft_config.keys())
        for adapter_name in adapter_names:
            keys = list(offload_index.keys())
            block_id = keys[0].split(".")[0] + "."  # for writing safetensors key,

            # replace original offload index keys with PeftModel keys
            for key in keys:
                suffix_pos = key.rfind(".")
                extended_prefix = prefix + key[:suffix_pos]
                module = dict(self.named_modules())[extended_prefix]
                if isinstance(module, BaseTunerLayer):
                    new_key = prefix + key[:suffix_pos] + ".base_layer" + key[suffix_pos:]
                else:
                    new_key = prefix + key
                offload_index[key]["weight_name"] = new_key
                offload_index[new_key] = offload_index[key]
                del offload_index[key]

            files_seen = set()
            # rename safetensors for dispatch
            for new_key in list(offload_index.keys()):
                fname = offload_index[new_key]["safetensors_file"]

                # make a new file name
                new_fname_list = list(fname.split(os.sep))
                for i, name in enumerate(new_fname_list):
                    if "--" in name:
                        new_fname_list[i] += "-peft"
                        break
                new_fname = os.path.join(*new_fname_list)

                if fname in files_seen:
                    continue
                safe_dict = {}
                with safe_open(fname, framework="pt") as f:
                    for safe_key in f.keys():
                        safe_tensor = f.get_tensor(safe_key)
                        metadata = f.metadata()
                        suffix_pos = safe_key.rfind(".")
                        extended_prefix = prefix + block_id + safe_key[:suffix_pos]
                        safe_module = dict(self.named_modules())[extended_prefix]
                        if isinstance(safe_module, BaseTunerLayer):
                            final_key = extended_prefix + ".base_layer" + safe_key[suffix_pos:]
                            lora_dict = {key: val for key, val in adapters_weights.items() if extended_prefix in key}

                            # add LoRA keys and values to disk offload
                            for lora_key, lora_val in lora_dict.items():
                                divide = lora_key.rfind(".")
                                new_key = lora_key[:divide] + f".{adapter_name}" + lora_key[divide:]
                                safe_dict[new_key] = lora_val
                        else:
                            final_key = prefix + block_id + safe_key
                        safe_dict[final_key] = safe_tensor
                    files_seen.add(new_fname)

                    # avoid overwriting original safetensors
                    for key in safe_dict.keys():
                        offload_index[key] = {"safetensors_file": new_fname, "weight_name": key}

                    base_name = os.path.dirname(new_fname)
                    if not os.path.exists(base_name):
                        os.makedirs(base_name)
                    safe_save_file(safe_dict, new_fname, metadata=metadata)

    def load_adapter(
        self,
        model_id: str,
        adapter_name: str,
        is_trainable: bool = False,
        torch_device: Optional[str] = None,
        **kwargs: Any,
    ):
        """
        Load a trained adapter into the model.

        The name for the new adapter should be unique.

        The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active
        adapter.

        Args:
            adapter_name (`str`):
                The name of the adapter to be added.
            peft_config ([`PeftConfig`]):
                The configuration of the adapter to be added.
            is_trainable (`bool`, *optional*, defaults to `False`):
                Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be
                used for inference.
            torch_device (`str`, *optional*, defaults to None):
                The device to load the adapter on. If `None`, the device will be inferred.
            kwargs: (`optional`):
                Additional arguments to modify the way the adapter is loaded, e.g. the token for Hugging Face Hub.
        """
        from .mapping import PEFT_TYPE_TO_CONFIG_MAPPING

        hf_hub_download_kwargs, kwargs = self._split_kwargs(kwargs)
        if torch_device is None:
            torch_device = infer_device()

        if adapter_name not in self.peft_config:
            # load the config
            peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[
                PeftConfig._get_peft_type(
                    model_id,
                    **hf_hub_download_kwargs,
                )
            ].from_pretrained(
                model_id,
                **hf_hub_download_kwargs,
            )
            if peft_config.is_prompt_learning and is_trainable:
                raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
            else:
                peft_config.inference_mode = not is_trainable
            self.add_adapter(adapter_name, peft_config)

        adapters_weights = load_peft_weights(model_id, device=torch_device, **hf_hub_download_kwargs)

        # load the weights into the model
        ignore_mismatched_sizes = kwargs.get("ignore_mismatched_sizes", False)
        load_result = set_peft_model_state_dict(
            self, adapters_weights, adapter_name=adapter_name, ignore_mismatched_sizes=ignore_mismatched_sizes
        )
        if (
            (getattr(self, "hf_device_map", None) is not None)
            and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
            and len(self.peft_config) == 1
        ):
            device_map = kwargs.get("device_map", "auto")
            max_memory = kwargs.get("max_memory", None)
            offload_dir = kwargs.get("offload_folder", None)
            offload_index = kwargs.get("offload_index", None)

            dispatch_model_kwargs = {}
            # Safety checker for previous `accelerate` versions
            # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
            if "offload_index" in inspect.signature(dispatch_model).parameters:
                dispatch_model_kwargs["offload_index"] = offload_index

            no_split_module_classes = self._no_split_modules

            if device_map != "sequential":
                max_memory = get_balanced_memory(
                    self,
                    max_memory=max_memory,
                    no_split_module_classes=no_split_module_classes,
                    low_zero=(device_map == "balanced_low_0"),
                )

            if isinstance(device_map, str):
                device_map = infer_auto_device_map(
                    self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
                )

            self._update_offload(offload_index, adapters_weights)
            dispatch_model_kwargs["offload_index"] = offload_index

            dispatch_model(
                self,
                device_map=device_map,
                offload_dir=offload_dir,
                **dispatch_model_kwargs,
            )

            hook = AlignDevicesHook(io_same_device=True)
            if self.peft_config[adapter_name].is_prompt_learning:
                remove_hook_from_submodules(self.prompt_encoder)
            add_hook_to_module(self.get_base_model(), hook)

        # Set model in evaluation mode to deactivate Dropout modules by default
        if not is_trainable:
            self.eval()
        return load_result

    def set_adapter(self, adapter_name: str) -> None:
        """
        Sets the active adapter.

        Only one adapter can be active at a time.

        Additionally, this function will set the specified adapter to trainable (i.e., requires_grad=True). If this is
        not desired, use the following code.

        ```py
        >>> for name, param in model_peft.named_parameters():
        ...     if ...:  # some check on name (ex. if 'lora' in name)
        ...         param.requires_grad = False
        ```

        Args:
            adapter_name (`str`):
                The name of the adapter to be set as active. The adapter must be loaded first.
        """
        if adapter_name not in self.peft_config:
            raise ValueError(f"Adapter {adapter_name} not found.")
        self.active_adapter = adapter_name
        if not self.peft_config[adapter_name].is_prompt_learning:
            self.base_model.set_adapter(adapter_name)
        _set_adapter(self, adapter_name)

    @property
    def base_model_torch_dtype(self):
        return getattr(self.base_model, "dtype", None)

    @property
    def active_peft_config(self):
        return self.peft_config[self.active_adapter]

    def create_or_update_model_card(self, output_dir: str):
        """
        Updates or create model card to include information about peft:
        1. Adds `peft` library tag
        2. Adds peft version
        3. Adds base model info
        4. Adds quantization information if it was used
        """

        filename = os.path.join(output_dir, "README.md")

        card = ModelCard.load(filename) if os.path.exists(filename) else ModelCard.from_template(ModelCardData())

        card.data["library_name"] = "peft"

        model_config = getattr(self, "config", None)
        if hasattr(model_config, "to_dict"):
            model_config = model_config.to_dict()
        if model_config is not None and "_name_or_path" in model_config:
            card.data["base_model"] = model_config["_name_or_path"]

        lines = card.text.splitlines()

        quantization_config = None
        if hasattr(model_config, "quantization_config"):
            quantization_config = self.config.quantization_config.to_dict()
        training_config_text = ""
        quantization_prefix = "The following `bitsandbytes` quantization config was used during training:"
        # Adds quantization information if it was used
        if quantization_config is not None:
            training_config_text += f"\n{quantization_prefix}\n"
            training_config_text += "\n".join([f"- {name}: {value}" for name, value in quantization_config.items()])
            training_config_text += "\n"

        training_procedure_heading = "## Training procedure"
        if quantization_prefix not in lines and bool(training_config_text):
            if training_procedure_heading in lines:
                lines.insert(lines.index(training_procedure_heading) + 2, training_config_text)
            else:
                lines.append(f"{training_procedure_heading}\n{training_config_text}")

        # Adds peft version
        framework_block_heading = "### Framework versions"
        if f"- PEFT {__version__}" not in lines:
            if framework_block_heading in lines:
                lines.insert(lines.index(framework_block_heading) + 2, f"- PEFT {__version__}")
            else:
                lines.append(f"{framework_block_heading}\n\n- PEFT {__version__}")

        card.text = "\n".join(lines)
        card.save(filename)


class PeftModelForSequenceClassification(PeftModel):
    """
    Peft model for sequence classification tasks.

    Args:
        model ([`~transformers.PreTrainedModel`]): Base transformer model.
        peft_config ([`PeftConfig`]): Peft config.

    **Attributes**:
        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
        - **cls_layer_name** (`str`) -- The name of the classification layer.

    Example:

        ```py
        >>> from transformers import AutoModelForSequenceClassification
        >>> from peft import PeftModelForSequenceClassification, get_peft_config

        >>> config = {
        ...     "peft_type": "PREFIX_TUNING",
        ...     "task_type": "SEQ_CLS",
        ...     "inference_mode": False,
        ...     "num_virtual_tokens": 20,
        ...     "token_dim": 768,
        ...     "num_transformer_submodules": 1,
        ...     "num_attention_heads": 12,
        ...     "num_layers": 12,
        ...     "encoder_hidden_size": 768,
        ...     "prefix_projection": False,
        ...     "postprocess_past_key_value_function": None,
        ... }

        >>> peft_config = get_peft_config(config)
        >>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
        >>> peft_model = PeftModelForSequenceClassification(model, peft_config)
        >>> peft_model.print_trainable_parameters()
        trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
        ```
    """

    def __init__(self, model: torch.nn.Module, peft_config: PeftConfig, adapter_name: str = "default") -> None:
        super().__init__(model, peft_config, adapter_name)

        classifier_module_names = ["classifier", "score"]
        if self.modules_to_save is None:
            self.modules_to_save = set(classifier_module_names)
        else:
            self.modules_to_save.update(classifier_module_names)

        if hasattr(peft_config, "modules_to_save"):
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = classifier_module_names[:]
            else:
                peft_config.modules_to_save.extend(classifier_module_names)

        for name, _ in self.base_model.named_children():
            if any(module_name in name for module_name in self.modules_to_save):
                self.cls_layer_name = name
                break

        # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
        _set_trainable(self, adapter_name)

    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
        """
        Add an adapter to the model based on the passed configuration.

        This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`].

        The name for the new adapter should be unique.

        The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active
        adapter.

        Args:
            adapter_name (`str`):
                The name of the adapter to be added.
            peft_config ([`PeftConfig`]):
                The configuration of the adapter to be added.
        """
        # ensure that additional adapters also add the classifier layer to modules_to_save
        if hasattr(peft_config, "modules_to_save"):
            classifier_module_names = ["classifier", "score"]
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = classifier_module_names[:]
            else:
                peft_config.modules_to_save.extend(classifier_module_names)

        return super().add_adapter(adapter_name, peft_config)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        task_ids=None,
        **kwargs,
    ):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        peft_config = self.active_peft_config
        if not peft_config.is_prompt_learning:
            with self._enable_peft_forward_hooks(**kwargs):
                kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                if peft_config.peft_type == PeftType.POLY:
                    kwargs["task_ids"] = task_ids
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    inputs_embeds=inputs_embeds,
                    labels=labels,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

        batch_size = _get_batch_size(input_ids, inputs_embeds)
        if attention_mask is not None:
            # concat prompt attention mask
            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
        if kwargs.get("position_ids", None) is not None:
            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
            kwargs["position_ids"] = None
        kwargs.update(
            {
                "attention_mask": attention_mask,
                "labels": labels,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
            }
        )

        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
        else:
            if kwargs.get("token_type_ids", None) is not None:
                kwargs["token_type_ids"] = torch.cat(
                    (
                        torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.word_embeddings.weight.device),
                        kwargs["token_type_ids"],
                    ),
                    dim=1,
                ).long()
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)
            prompts = self.get_prompt(batch_size=batch_size, task_ids=task_ids)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)

    def _prefix_tuning_forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        **kwargs,
    ):
        batch_size = _get_batch_size(input_ids, inputs_embeds)
        past_key_values = self.get_prompt(batch_size)
        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
        kwargs.update(
            {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "inputs_embeds": inputs_embeds,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
                "past_key_values": past_key_values,
            }
        )
        if "past_key_values" in fwd_params:
            return self.base_model(labels=labels, **kwargs)
        else:
            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
            if "past_key_values" not in fwd_params:
                raise ValueError("Model does not support past key values which are required for prefix tuning.")
            outputs = transformer_backbone_name(**kwargs)
            pooled_output = outputs[1] if len(outputs) > 1 else outputs[0]
            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
                pooled_output = self.base_model.dropout(pooled_output)
            logits = self.base_model.get_submodule(self.cls_layer_name)(pooled_output)

            loss = None
            if labels is not None:
                if self.config.problem_type is None:
                    if self.base_model.num_labels == 1:
                        self.config.problem_type = "regression"
                    elif self.base_model.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                        self.config.problem_type = "single_label_classification"
                    else:
                        self.config.problem_type = "multi_label_classification"

                if self.config.problem_type == "regression":
                    loss_fct = MSELoss()
                    if self.base_model.num_labels == 1:
                        loss = loss_fct(logits.squeeze(), labels.squeeze())
                    else:
                        loss = loss_fct(logits, labels)
                elif self.config.problem_type == "single_label_classification":
                    loss_fct = CrossEntropyLoss()
                    loss = loss_fct(logits.view(-1, self.base_model.num_labels), labels.view(-1))
                elif self.config.problem_type == "multi_label_classification":
                    loss_fct = BCEWithLogitsLoss()
                    loss = loss_fct(logits, labels)
            if not return_dict:
                output = (logits,) + outputs[2:]
                return ((loss,) + output) if loss is not None else output

            return SequenceClassifierOutput(
                loss=loss,
                logits=logits,
                hidden_states=outputs.hidden_states,
                attentions=outputs.attentions,
            )


class PeftModelForCausalLM(PeftModel):
    """
    Peft model for causal language modeling.

    Args:
        model ([`~transformers.PreTrainedModel`]): Base transformer model.
        peft_config ([`PeftConfig`]): Peft config.


    Example:

        ```py
        >>> from transformers import AutoModelForCausalLM
        >>> from peft import PeftModelForCausalLM, get_peft_config

        >>> config = {
        ...     "peft_type": "PREFIX_TUNING",
        ...     "task_type": "CAUSAL_LM",
        ...     "inference_mode": False,
        ...     "num_virtual_tokens": 20,
        ...     "token_dim": 1280,
        ...     "num_transformer_submodules": 1,
        ...     "num_attention_heads": 20,
        ...     "num_layers": 36,
        ...     "encoder_hidden_size": 1280,
        ...     "prefix_projection": False,
        ...     "postprocess_past_key_value_function": None,
        ... }

        >>> peft_config = get_peft_config(config)
        >>> model = AutoModelForCausalLM.from_pretrained("gpt2-large")
        >>> peft_model = PeftModelForCausalLM(model, peft_config)
        >>> peft_model.print_trainable_parameters()
        trainable params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544
        ```
    """

    def __init__(self, model: torch.nn.Module, peft_config: PeftConfig, adapter_name: str = "default") -> None:
        super().__init__(model, peft_config, adapter_name)
        self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        task_ids=None,
        **kwargs,
    ):
        peft_config = self.active_peft_config
        if not peft_config.is_prompt_learning:
            if self.base_model.config.model_type == "mpt":
                if inputs_embeds is not None:
                    raise AssertionError("forward in MPTForCausalLM does not support inputs_embeds")
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    labels=labels,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

            if peft_config.peft_type == PeftType.POLY:
                kwargs["task_ids"] = task_ids

            with self._enable_peft_forward_hooks(**kwargs):
                kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    inputs_embeds=inputs_embeds,
                    labels=labels,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

        batch_size = _get_batch_size(input_ids, inputs_embeds)
        if attention_mask is not None:
            # concat prompt attention mask
            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)

        if kwargs.get("position_ids", None) is not None:
            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
            kwargs["position_ids"] = None
        if kwargs.get("token_type_ids", None) is not None:
            warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
            kwargs["token_type_ids"] = None
        kwargs.update(
            {
                "attention_mask": attention_mask,
                "labels": labels,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
            }
        )

        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            past_key_values = self.get_prompt(batch_size)
            return self.base_model(
                input_ids=input_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, **kwargs
            )
        else:
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)
            # concat prompt labels
            if labels is not None:
                prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(labels.device)
                kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
            prompts = self.get_prompt(batch_size=batch_size, task_ids=task_ids)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)

    def generate(self, *args, **kwargs):
        peft_config = self.active_peft_config
        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
        if hasattr(self.base_model, "model"):
            self.base_model.model.generation_config = self.generation_config
        else:
            self.base_model.generation_config = self.generation_config
        try:
            if not peft_config.is_prompt_learning:
                with self._enable_peft_forward_hooks(*args, **kwargs):
                    kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                    outputs = self.base_model.generate(*args, **kwargs)
            else:
                outputs = self.base_model.generate(**kwargs)
        except:
            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
            raise
        else:
            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
            return outputs

    def prepare_inputs_for_generation(self, *args, task_ids: Optional[torch.Tensor] = None, **kwargs):
        peft_config = self.active_peft_config
        model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)

        # https://github.com/huggingface/transformers/pull/26681/ introduced new cache format
        # for some architectures which requires a special fix for prompt tuning etc.
        # TODO: starting with transformers 4.38, all architectures should support caching.
        uses_transformers_4_38 = packaging.version.parse(transformers.__version__) >= packaging.version.parse("4.38.0")
        uses_transformers_4_36 = packaging.version.parse(transformers.__version__) >= packaging.version.parse("4.36.0")
        transformers_new_cache_archs = ["llama", "mistral", "persimmon", "phi"]
        uses_cache = uses_transformers_4_38 or (
            uses_transformers_4_36 and self.base_model.config.model_type in transformers_new_cache_archs
        )

        if peft_config.peft_type == PeftType.POLY:
            model_kwargs["task_ids"] = task_ids
        if peft_config.is_prompt_learning:
            if uses_cache and (model_kwargs["past_key_values"] is not None):
                # change in the logic of `prepare_inputs_for_generation` makes the below code necessary
                # In prompt learning methods, past key values are longer when compared to the `input_ids`.
                # As such only consider the last input ids in the autogressive generation phase.
                if model_kwargs["past_key_values"][0][0].shape[-2] >= model_kwargs["input_ids"].shape[1]:
                    model_kwargs["input_ids"] = model_kwargs["input_ids"][:, -1:]

            if model_kwargs.get("attention_mask", None) is not None:
                size = model_kwargs["input_ids"].shape[0], peft_config.num_virtual_tokens
                prefix_attention_mask = torch.ones(size).to(model_kwargs["input_ids"].device)
                model_kwargs["attention_mask"] = torch.cat(
                    (prefix_attention_mask, model_kwargs["attention_mask"]), dim=1
                )

            if model_kwargs.get("position_ids", None) is not None:
                warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
                model_kwargs["position_ids"] = None

            if kwargs.get("token_type_ids", None) is not None:
                warnings.warn(
                    "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
                )
                kwargs["token_type_ids"] = None

            if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
                past_key_values = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
                model_kwargs["past_key_values"] = past_key_values
            else:
                if model_kwargs["past_key_values"] is None:
                    inputs_embeds = self.word_embeddings(model_kwargs["input_ids"])
                    prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0], task_ids=task_ids)
                    prompts = prompts.to(inputs_embeds.dtype)
                    model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1)
                    model_kwargs["input_ids"] = None

        # For transformers>=4.38.0 - for some architectures such as Llama, `cache_position` is
        # passed in the forward pass to keep track of the position ids of the cache. We have to
        # pop that from `model_kwargs` as `cache_position` is properly created by the model, using the passed
        # `inputs_embeds`: https://github.com/huggingface/transformers/blob/593230f0a1150ea9c0477b9d859f25daf73c8c33/src/transformers/models/llama/modeling_llama.py#L956
        _ = model_kwargs.pop("cache_position", None)

        return model_kwargs


class PeftModelForSeq2SeqLM(PeftModel):
    """
    Peft model for sequence-to-sequence language modeling.

    Args:
        model ([`~transformers.PreTrainedModel`]): Base transformer model.
        peft_config ([`PeftConfig`]): Peft config.


    Example:

        ```py
        >>> from transformers import AutoModelForSeq2SeqLM
        >>> from peft import PeftModelForSeq2SeqLM, get_peft_config

        >>> config = {
        ...     "peft_type": "LORA",
        ...     "task_type": "SEQ_2_SEQ_LM",
        ...     "inference_mode": False,
        ...     "r": 8,
        ...     "target_modules": ["q", "v"],
        ...     "lora_alpha": 32,
        ...     "lora_dropout": 0.1,
        ...     "fan_in_fan_out": False,
        ...     "enable_lora": None,
        ...     "bias": "none",
        ... }

        >>> peft_config = get_peft_config(config)
        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
        >>> peft_model = PeftModelForSeq2SeqLM(model, peft_config)
        >>> peft_model.print_trainable_parameters()
        trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566
        ```
    """

    def __init__(self, model: torch.nn.Module, peft_config: PeftConfig, adapter_name: str = "default") -> None:
        super().__init__(model, peft_config, adapter_name)
        self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
        self.base_model_prepare_encoder_decoder_kwargs_for_generation = (
            self.base_model._prepare_encoder_decoder_kwargs_for_generation
        )

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        decoder_inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        task_ids=None,
        **kwargs,
    ):
        peft_config = self.active_peft_config
        if not peft_config.is_prompt_learning:
            if peft_config.peft_type == PeftType.POLY:
                kwargs["task_ids"] = task_ids

            with self._enable_peft_forward_hooks(**kwargs):
                kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    inputs_embeds=inputs_embeds,
                    decoder_input_ids=decoder_input_ids,
                    decoder_attention_mask=decoder_attention_mask,
                    decoder_inputs_embeds=decoder_inputs_embeds,
                    labels=labels,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

        batch_size = _get_batch_size(input_ids, inputs_embeds)
        if decoder_attention_mask is not None:
            # concat prompt attention mask
            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
                decoder_attention_mask.device
            )
            if peft_config.peft_type not in [PeftType.PROMPT_TUNING, PeftType.P_TUNING]:
                decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1)

        if kwargs.get("position_ids", None) is not None:
            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
            kwargs["position_ids"] = None
        if kwargs.get("token_type_ids", None) is not None:
            warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
            kwargs["token_type_ids"] = None
        kwargs.update(
            {
                "attention_mask": attention_mask,
                "decoder_attention_mask": decoder_attention_mask,
                "labels": labels,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
            }
        )

        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            past_key_values = self.get_prompt(batch_size)
            return self.base_model(
                input_ids=input_ids,
                decoder_input_ids=decoder_input_ids,
                decoder_inputs_embeds=decoder_inputs_embeds,
                past_key_values=past_key_values,
                **kwargs,
            )
        elif peft_config.peft_type in [PeftType.PROMPT_TUNING, PeftType.P_TUNING]:
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)

            if attention_mask is not None:
                # concat prompt attention mask
                prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
                    attention_mask.device
                )
                kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1)

            prompts = self.get_prompt(batch_size=batch_size)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)

            return self.base_model(
                inputs_embeds=inputs_embeds,
                decoder_input_ids=decoder_input_ids,
                decoder_inputs_embeds=decoder_inputs_embeds,
                **kwargs,
            )
        else:
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)
            if decoder_inputs_embeds is None and decoder_input_ids is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )
                decoder_inputs_embeds = self.word_embeddings(decoder_input_ids)

            if attention_mask is not None:
                # concat prompt attention mask
                prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
                    attention_mask.device
                )
                kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1)
            # concat prompt labels
            if labels is not None:
                if peft_config.num_transformer_submodules == 1:
                    kwargs["labels"] = labels
                elif peft_config.num_transformer_submodules == 2:
                    prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(labels.device)
                    kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
            prompts = self.get_prompt(batch_size=batch_size, task_ids=task_ids)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)
            if peft_config.num_transformer_submodules == 1:
                return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
            elif peft_config.num_transformer_submodules == 2:
                decoder_inputs_embeds = torch.cat(
                    (prompts[:, peft_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1
                )
                return self.base_model(
                    inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, **kwargs
                )

    def generate(self, **kwargs):
        peft_config = self.active_peft_config
        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
        self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
            self._prepare_encoder_decoder_kwargs_for_generation
        )
        try:
            if not peft_config.is_prompt_learning:
                with self._enable_peft_forward_hooks(**kwargs):
                    kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                    outputs = self.base_model.generate(**kwargs)
            else:
                if "input_ids" not in kwargs:
                    raise ValueError("input_ids must be provided for Peft model generation")
                if kwargs.get("position_ids", None) is not None:
                    warnings.warn(
                        "Position ids are not supported for parameter efficient tuning. Ignoring position ids."
                    )
                    kwargs["position_ids"] = None
                if kwargs.get("token_type_ids", None) is not None:
                    warnings.warn(
                        "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
                    )
                    kwargs["token_type_ids"] = None

                if peft_config.peft_type == PeftType.PREFIX_TUNING:
                    outputs = self.base_model.generate(**kwargs)
                elif peft_config.peft_type in [
                    PeftType.PROMPT_TUNING,
                    PeftType.P_TUNING,
                    PeftType.MULTITASK_PROMPT_TUNING,
                ]:
                    kwargs = deepcopy(kwargs)

                    if "encoder_outputs" in kwargs:
                        del kwargs["encoder_outputs"]
                        warnings.warn(
                            "`encoder_outputs` should not be passed to `generate` when using prompt tuning. Ignoring it."
                        )

                    input_ids = kwargs.pop("input_ids")
                    inputs_embeds = self.word_embeddings(input_ids)
                    batch_size = inputs_embeds.shape[0]
                    prompts = self.get_prompt(batch_size=batch_size, task_ids=kwargs.pop("task_ids", None))
                    prompts = prompts.to(inputs_embeds.dtype)

                    inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)
                    kwargs["inputs_embeds"] = inputs_embeds

                    if "attention_mask" in kwargs:
                        prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
                            kwargs["attention_mask"].device
                        )
                        kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1)

                    return self.base_model.generate(**kwargs)
                else:
                    raise NotImplementedError
        except:
            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
            self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
                self.base_model_prepare_encoder_decoder_kwargs_for_generation
            )
            raise
        else:
            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
            self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
                self.base_model_prepare_encoder_decoder_kwargs_for_generation
            )
            return outputs

    def prepare_inputs_for_generation(self, *args, task_ids: torch.Tensor = None, **kwargs):
        peft_config = self.active_peft_config
        model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
        if peft_config.peft_type == PeftType.POLY:
            model_kwargs["task_ids"] = task_ids
        if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
            batch_size = model_kwargs["decoder_input_ids"].shape[0]
            past_key_values = self.get_prompt(batch_size)
            model_kwargs["past_key_values"] = past_key_values

        return model_kwargs


class PeftModelForTokenClassification(PeftModel):
    """
    Peft model for token classification tasks.

    Args:
        model ([`~transformers.PreTrainedModel`]): Base transformer model.
        peft_config ([`PeftConfig`]): Peft config.

    **Attributes**:
        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
        - **cls_layer_name** (`str`) -- The name of the classification layer.

    Example:

        ```py
        >>> from transformers import AutoModelForSequenceClassification
        >>> from peft import PeftModelForTokenClassification, get_peft_config

        >>> config = {
        ...     "peft_type": "PREFIX_TUNING",
        ...     "task_type": "TOKEN_CLS",
        ...     "inference_mode": False,
        ...     "num_virtual_tokens": 20,
        ...     "token_dim": 768,
        ...     "num_transformer_submodules": 1,
        ...     "num_attention_heads": 12,
        ...     "num_layers": 12,
        ...     "encoder_hidden_size": 768,
        ...     "prefix_projection": False,
        ...     "postprocess_past_key_value_function": None,
        ... }

        >>> peft_config = get_peft_config(config)
        >>> model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")
        >>> peft_model = PeftModelForTokenClassification(model, peft_config)
        >>> peft_model.print_trainable_parameters()
        trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
        ```
    """

    def __init__(self, model: torch.nn.Module, peft_config: PeftConfig = None, adapter_name: str = "default") -> None:
        super().__init__(model, peft_config, adapter_name)

        classifier_module_names = ["classifier", "score"]
        if self.modules_to_save is None:
            self.modules_to_save = set(classifier_module_names)
        else:
            self.modules_to_save.update(classifier_module_names)

        if hasattr(peft_config, "modules_to_save"):
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = classifier_module_names[:]
            else:
                peft_config.modules_to_save.extend(classifier_module_names)

        for name, _ in self.base_model.named_children():
            if any(module_name in name for module_name in self.modules_to_save):
                self.cls_layer_name = name
                break

        # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
        _set_trainable(self, adapter_name)

    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
        """
        Add an adapter to the model based on the passed configuration.

        This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`].

        The name for the new adapter should be unique.

        The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active
        adapter.

        Args:
            adapter_name (`str`):
                The name of the adapter to be added.
            peft_config ([`PeftConfig`]):
                The configuration of the adapter to be added.
        """
        # ensure that additional adapters also add the classifier layer to modules_to_save
        if hasattr(peft_config, "modules_to_save"):
            classifier_module_names = ["classifier", "score"]
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = classifier_module_names[:]
            else:
                peft_config.modules_to_save.extend(classifier_module_names)

        return super().add_adapter(adapter_name, peft_config)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        task_ids=None,
        **kwargs,
    ):
        peft_config = self.active_peft_config
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if not peft_config.is_prompt_learning:
            with self._enable_peft_forward_hooks(**kwargs):
                kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                if peft_config.peft_type == PeftType.POLY:
                    kwargs["task_ids"] = task_ids
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    inputs_embeds=inputs_embeds,
                    labels=labels,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

        batch_size = _get_batch_size(input_ids, inputs_embeds)
        if attention_mask is not None:
            # concat prompt attention mask
            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
        if kwargs.get("position_ids", None) is not None:
            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
            kwargs["position_ids"] = None
        kwargs.update(
            {
                "attention_mask": attention_mask,
                "labels": labels,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
            }
        )

        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
        else:
            if kwargs.get("token_type_ids", None) is not None:
                kwargs["token_type_ids"] = torch.cat(
                    (
                        torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.word_embeddings.weight.device),
                        kwargs["token_type_ids"],
                    ),
                    dim=1,
                ).long()
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)
            prompts = self.get_prompt(batch_size=batch_size, task_ids=task_ids)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)

    def _prefix_tuning_forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        **kwargs,
    ):
        batch_size = _get_batch_size(input_ids, inputs_embeds)
        past_key_values = self.get_prompt(batch_size)
        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
        kwargs.update(
            {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "inputs_embeds": inputs_embeds,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
                "past_key_values": past_key_values,
            }
        )
        if "past_key_values" in fwd_params:
            return self.base_model(labels=labels, **kwargs)
        else:
            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
            if "past_key_values" not in fwd_params:
                raise ValueError("Model does not support past key values which are required for prefix tuning.")
            outputs = transformer_backbone_name(**kwargs)
            sequence_output = outputs[0]
            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
                sequence_output = self.base_model.dropout(sequence_output)
            logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)

            loss = None
            if labels is not None:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

            if not return_dict:
                output = (logits,) + outputs[2:]
                return ((loss,) + output) if loss is not None else output

            return TokenClassifierOutput(
                loss=loss,
                logits=logits,
                hidden_states=outputs.hidden_states,
                attentions=outputs.attentions,
            )


class PeftModelForQuestionAnswering(PeftModel):
    """
    Peft model for extractive question answering.

    Args:
        model ([`~transformers.PreTrainedModel`]): Base transformer model.
        peft_config ([`PeftConfig`]): Peft config.

    **Attributes**:
        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
        - **cls_layer_name** (`str`) -- The name of the classification layer.

    Example:

        ```py
        >>> from transformers import AutoModelForQuestionAnswering
        >>> from peft import PeftModelForQuestionAnswering, get_peft_config

        >>> config = {
        ...     "peft_type": "LORA",
        ...     "task_type": "QUESTION_ANS",
        ...     "inference_mode": False,
        ...     "r": 16,
        ...     "target_modules": ["query", "value"],
        ...     "lora_alpha": 32,
        ...     "lora_dropout": 0.05,
        ...     "fan_in_fan_out": False,
        ...     "bias": "none",
        ... }

        >>> peft_config = get_peft_config(config)
        >>> model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased")
        >>> peft_model = PeftModelForQuestionAnswering(model, peft_config)
        >>> peft_model.print_trainable_parameters()
        trainable params: 592900 || all params: 108312580 || trainable%: 0.5473971721475013
        ```
    """

    def __init__(self, model: torch.nn.Module, peft_config: PeftConfig, adapter_name: str = "default") -> None:
        super().__init__(model, peft_config, adapter_name)

        qa_module_names = ["qa_outputs"]
        if self.modules_to_save is None:
            self.modules_to_save = set(qa_module_names)
        else:
            self.modules_to_save.update(qa_module_names)

        if hasattr(peft_config, "modules_to_save"):
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = qa_module_names[:]
            else:
                peft_config.modules_to_save.extend(qa_module_names)

        for name, _ in self.base_model.named_children():
            if any(module_name in name for module_name in self.modules_to_save):
                self.cls_layer_name = name
                break

        # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
        _set_trainable(self, adapter_name)

    def add_adapter(self, adapter_name: str, peft_config: PeftConfig) -> None:
        """
        Add an adapter to the model based on the passed configuration.

        This adapter is not trained. To load a trained adapter, check out [`PeftModel.load_adapter`].

        The name for the new adapter should be unique.

        The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active
        adapter.

        Args:
            adapter_name (`str`):
                The name of the adapter to be added.
            peft_config ([`PeftConfig`]):
                The configuration of the adapter to be added.
        """
        # ensure that additional adapters also add the classifier layer to modules_to_save
        if hasattr(peft_config, "modules_to_save"):
            qa_module_names = ["qa_outputs"]
            if peft_config.modules_to_save is None:
                peft_config.modules_to_save = qa_module_names[:]
            else:
                peft_config.modules_to_save.extend(qa_module_names)

        return super().add_adapter(adapter_name, peft_config)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        inputs_embeds=None,
        start_positions=None,
        end_positions=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        task_ids=None,
        **kwargs,
    ):
        peft_config = self.active_peft_config
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if not peft_config.is_prompt_learning:
            if peft_config.peft_type == PeftType.POLY:
                kwargs["task_ids"] = task_ids

            with self._enable_peft_forward_hooks(**kwargs):
                kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    inputs_embeds=inputs_embeds,
                    start_positions=start_positions,
                    end_positions=end_positions,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

        batch_size = _get_batch_size(input_ids, inputs_embeds)
        if attention_mask is not None:
            # concat prompt attention mask
            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
        if kwargs.get("position_ids", None) is not None:
            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
            kwargs["position_ids"] = None
        kwargs.update(
            {
                "attention_mask": attention_mask,
                "start_positions": start_positions,
                "end_positions": end_positions,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
            }
        )

        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
        else:
            if kwargs.get("token_type_ids", None) is not None:
                kwargs["token_type_ids"] = torch.cat(
                    (
                        torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.word_embeddings.weight.device),
                        kwargs["token_type_ids"],
                    ),
                    dim=1,
                ).long()
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)
            prompts = self.get_prompt(batch_size=batch_size)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)

    def _prefix_tuning_forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        start_positions=None,
        end_positions=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        **kwargs,
    ):
        batch_size = _get_batch_size(input_ids, inputs_embeds)
        past_key_values = self.get_prompt(batch_size)
        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
        kwargs.update(
            {
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "inputs_embeds": inputs_embeds,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
                "past_key_values": past_key_values,
            }
        )
        if "past_key_values" in fwd_params:
            return self.base_model(start_positions=start_positions, end_positions=end_positions, **kwargs)
        else:
            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
            if "past_key_values" not in fwd_params:
                raise ValueError("Model does not support past key values which are required for prefix tuning.")
            outputs = transformer_backbone_name(**kwargs)
            sequence_output = outputs[0]
            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
                sequence_output = self.base_model.dropout(sequence_output)
            logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)
            start_logits, end_logits = logits.split(1, dim=-1)
            start_logits = start_logits.squeeze(-1).contiguous()
            end_logits = end_logits.squeeze(-1).contiguous()

            total_loss = None
            if start_positions is not None and end_positions is not None:
                # If we are on multi-GPU, split add a dimension
                if len(start_positions.size()) > 1:
                    start_positions = start_positions.squeeze(-1)
                if len(end_positions.size()) > 1:
                    end_positions = end_positions.squeeze(-1)
                # sometimes the start/end positions are outside our model inputs, we ignore these terms
                ignored_index = start_logits.size(1)
                start_positions = start_positions.clamp(0, ignored_index)
                end_positions = end_positions.clamp(0, ignored_index)

                loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
                start_loss = loss_fct(start_logits, start_positions)
                end_loss = loss_fct(end_logits, end_positions)
                total_loss = (start_loss + end_loss) / 2

            if not return_dict:
                output = (start_logits, end_logits) + outputs[2:]
                return ((total_loss,) + output) if total_loss is not None else output

            return QuestionAnsweringModelOutput(
                loss=total_loss,
                start_logits=start_logits,
                end_logits=end_logits,
                hidden_states=outputs.hidden_states,
                attentions=outputs.attentions,
            )


class PeftModelForFeatureExtraction(PeftModel):
    """
    Peft model for extracting features/embeddings from transformer models

    Args:
        model ([`~transformers.PreTrainedModel`]): Base transformer model.
        peft_config ([`PeftConfig`]): Peft config.

    **Attributes**:
        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.

    Example:

        ```py
        >>> from transformers import AutoModel
        >>> from peft import PeftModelForFeatureExtraction, get_peft_config

        >>> config = {
        ...     "peft_type": "LORA",
        ...     "task_type": "FEATURE_EXTRACTION",
        ...     "inference_mode": False,
        ...     "r": 16,
        ...     "target_modules": ["query", "value"],
        ...     "lora_alpha": 32,
        ...     "lora_dropout": 0.05,
        ...     "fan_in_fan_out": False,
        ...     "bias": "none",
        ... }
        >>> peft_config = get_peft_config(config)
        >>> model = AutoModel.from_pretrained("bert-base-cased")
        >>> peft_model = PeftModelForFeatureExtraction(model, peft_config)
        >>> peft_model.print_trainable_parameters()
        ```
    """

    def __init__(self, model: torch.nn.Module, peft_config: PeftConfig, adapter_name: str = "default"):
        super().__init__(model, peft_config, adapter_name)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        task_ids=None,
        **kwargs,
    ):
        peft_config = self.active_peft_config
        if not peft_config.is_prompt_learning:
            if peft_config.peft_type == PeftType.POLY:
                kwargs["task_ids"] = task_ids

            with self._enable_peft_forward_hooks(**kwargs):
                kwargs = {k: v for k, v in kwargs.items() if k not in self.special_peft_forward_args}
                return self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    inputs_embeds=inputs_embeds,
                    output_attentions=output_attentions,
                    output_hidden_states=output_hidden_states,
                    return_dict=return_dict,
                    **kwargs,
                )

        batch_size = _get_batch_size(input_ids, inputs_embeds)
        if attention_mask is not None:
            # concat prompt attention mask
            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)

        if kwargs.get("position_ids", None) is not None:
            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
            kwargs["position_ids"] = None
        if kwargs.get("token_type_ids", None) is not None:
            warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
            kwargs["token_type_ids"] = None
        kwargs.update(
            {
                "attention_mask": attention_mask,
                "output_attentions": output_attentions,
                "output_hidden_states": output_hidden_states,
                "return_dict": return_dict,
            }
        )

        if peft_config.peft_type == PeftType.PREFIX_TUNING:
            past_key_values = self.get_prompt(batch_size)
            return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs)
        else:
            if inputs_embeds is None:
                inputs_embeds = self.word_embeddings(input_ids)
            prompts = self.get_prompt(batch_size=batch_size)
            prompts = prompts.to(inputs_embeds.dtype)
            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
