# coding=utf-8
# 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
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# 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.
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from __future__ import annotations

import re
import warnings
from dataclasses import asdict
from enum import Enum
from typing import List, Optional

import torch
from torch import nn
from transformers.pytorch_utils import Conv1D

from peft.import_utils import is_bnb_4bit_available, is_bnb_available
from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists
from peft.utils import (
    TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING,
    TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING,
    ModulesToSaveWrapper,
    _get_submodules,
)

from .layer import Conv2d, IA3Layer, Linear


class IA3Model(BaseTuner):
    """
    Creates a Infused Adapter by Inhibiting and Amplifying Inner Activations ((IA)^3) model from a pretrained
    transformers model. The method is described in detail in https://arxiv.org/abs/2205.05638

    Args:
        model ([`~transformers.PreTrainedModel`]): The model to be adapted.
        config ([`IA3Config`]): The configuration of the (IA)^3 model.
        adapter_name (`str`): The name of the adapter, defaults to `"default"`.

    Returns:
        `torch.nn.Module`: The (IA)^3 model.

    Example:

        ```py
        >>> from transformers import AutoModelForSeq2SeqLM, ia3Config
        >>> from peft import IA3Model, IA3Config

        >>> config = IA3Config(
        ...     peft_type="IA3",
        ...     task_type="SEQ_2_SEQ_LM",
        ...     target_modules=["k", "v", "w0"],
        ...     feedforward_modules=["w0"],
        ... )

        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
        >>> ia3_model = IA3Model(config, model)
        ```

    **Attributes**:
        - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
        - **peft_config** ([`ia3Config`]): The configuration of the (IA)^3 model.
    """

    prefix: str = "ia3_"

    def __init__(self, model, config, adapter_name):
        super().__init__(model, config, adapter_name)

    @staticmethod
    def _create_new_module(ia3_config, adapter_name, target, **kwargs):
        # avoid eager bnb import
        if is_bnb_available():
            import bitsandbytes as bnb

            from .bnb import Linear8bitLt

        if is_bnb_4bit_available():
            from .bnb import Linear4bit

        loaded_in_8bit = kwargs.pop("loaded_in_8bit", False)
        loaded_in_4bit = kwargs.pop("loaded_in_4bit", False)
        is_feedforward = kwargs.pop("is_feedforward", False)

        if isinstance(target, BaseTunerLayer):
            target_base_layer = target.get_base_layer()
        else:
            target_base_layer = target

        if loaded_in_8bit and isinstance(target_base_layer, bnb.nn.Linear8bitLt):
            eightbit_kwargs = kwargs.copy()
            eightbit_kwargs.update(
                {
                    "has_fp16_weights": target_base_layer.state.has_fp16_weights,
                    "memory_efficient_backward": target_base_layer.state.memory_efficient_backward,
                    "threshold": target_base_layer.state.threshold,
                    "index": target_base_layer.index,
                }
            )
            new_module = Linear8bitLt(target, adapter_name, is_feedforward=is_feedforward, **eightbit_kwargs)
        elif loaded_in_4bit and isinstance(target_base_layer, bnb.nn.Linear4bit):
            fourbit_kwargs = kwargs.copy()
            fourbit_kwargs.update(
                {
                    "compute_dtype": target_base_layer.compute_dtype,
                    "compress_statistics": target_base_layer.weight.compress_statistics,
                    "quant_type": target_base_layer.weight.quant_type,
                }
            )
            new_module = Linear4bit(target, adapter_name, is_feedforward=is_feedforward, **fourbit_kwargs)
        elif isinstance(target, torch.nn.Conv2d):
            new_module = Conv2d(target, adapter_name, is_feedforward=is_feedforward, **kwargs)
        elif isinstance(target_base_layer, torch.nn.Linear):
            if kwargs["fan_in_fan_out"]:
                warnings.warn(
                    "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
                    "Setting fan_in_fan_out to False."
                )
                kwargs["fan_in_fan_out"] = ia3_config.fan_in_fan_out = False
            new_module = Linear(target, adapter_name, is_feedforward=is_feedforward, **kwargs)
        elif isinstance(target_base_layer, Conv1D):
            if not kwargs["fan_in_fan_out"]:
                warnings.warn(
                    "fan_in_fan_out is set to False but the target module is `Conv1D`. "
                    "Setting fan_in_fan_out to True."
                )
                kwargs["fan_in_fan_out"] = ia3_config.fan_in_fan_out = True
            new_module = Linear(
                target, adapter_name, is_feedforward=is_feedforward, is_target_conv_1d_layer=True, **kwargs
            )
        else:
            raise ValueError(
                f"Target module {target} is not supported. "
                f"Currently, only `torch.nn.Linear`, `torch.nn.Conv2d`, and `Conv1D` are supported."
            )
        return new_module

    @staticmethod
    def _check_target_module_exists(ia3_config, key):
        return check_target_module_exists(ia3_config, key)

    def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
        for n, p in model.named_parameters():
            if self.prefix not in n:
                p.requires_grad = False

    def _create_and_replace(
        self,
        ia3_config,
        adapter_name,
        target,
        target_name,
        parent,
        current_key,
    ):
        # check if target module is in feedforward_modules
        is_feedforward = self._check_target_module_feedforward(ia3_config, current_key)

        kwargs = {
            "fan_in_fan_out": ia3_config.fan_in_fan_out,
            "init_ia3_weights": ia3_config.init_ia3_weights,
            "is_feedforward": is_feedforward,
            "loaded_in_8bit": getattr(self.model, "is_loaded_in_8bit", False),
            "loaded_in_4bit": getattr(self.model, "is_loaded_in_4bit", False),
        }

        if isinstance(target, IA3Layer):
            target.update_layer(
                adapter_name,
                ia3_config.init_ia3_weights,
            )
        else:
            new_module = self._create_new_module(ia3_config, adapter_name, target, **kwargs)
            if adapter_name != self.active_adapter:
                # adding an additional adapter: it is not automatically trainable
                new_module.requires_grad_(False)
            self._replace_module(parent, target_name, new_module, target)

    @staticmethod
    def _check_target_module_feedforward(ia3_config, key) -> bool:
        """
        A helper private method that checks if the target module `key` matches with a feedforward module specified in
        `ia3_config`
        """
        if isinstance(ia3_config.feedforward_modules, str):
            is_feedforward = bool(re.fullmatch(ia3_config.feedforward_modules, key))
        else:
            is_feedforward = any(key.endswith(target_key) for target_key in ia3_config.feedforward_modules)
        return is_feedforward

    def _replace_module(self, parent, child_name, new_module, child):
        setattr(parent, child_name, new_module)

        # child layer wraps the original module, unpack it
        if hasattr(child, "base_layer"):
            child = child.base_layer

        # layers with base_layer don't need the weight to be copied, as they have a reference already
        if not hasattr(new_module, "base_layer"):
            new_module.weight = child.weight
            if hasattr(child, "bias"):
                new_module.bias = child.bias

        if getattr(child, "state", None) is not None:
            if hasattr(new_module, "base_layer"):
                new_module.base_layer.state = child.state
            else:
                new_module.state = child.state
            new_module.to(child.weight.device)

        # dispatch to correct device
        for name, module in new_module.named_modules():
            if self.prefix in name:
                module.to(child.weight.device)

    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.model, name)

    def get_peft_config_as_dict(self, inference: bool = False):
        config_dict = {}
        for key, value in self.peft_config.items():
            config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
            if inference:
                config["inference_mode"] = True
        config_dict[key] = config
        return config

    def _set_adapter_layers(self, enabled=True):
        for module in self.model.modules():
            if isinstance(module, (IA3Layer, ModulesToSaveWrapper)):
                module.enable_adapters(enabled)

    def enable_adapter_layers(self) -> None:
        """Enable all adapters.

        Call this if you have previously disabled all adapters and want to re-enable them.
        """
        self._set_adapter_layers(enabled=True)

    def disable_adapter_layers(self) -> None:
        """Disable all adapters.

        When disabling all adapters, the model output corresponds to the output of the base model.
        """
        self._set_adapter_layers(enabled=False)

    def set_adapter(self, adapter_name: str | list[str]) -> None:
        """Set the active adapter(s).

        Args:
            adapter_name (`str` or `list[str]`): Name of the adapter(s) to be activated.
        """
        for module in self.model.modules():
            if isinstance(module, IA3Layer):
                if module.merged:
                    warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
                    module.unmerge()
                module.set_adapter(adapter_name)

    def _prepare_adapter_config(self, peft_config, model_config):
        if peft_config.target_modules is None:
            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING:
                raise ValueError("Please specify `target_modules` in `peft_config`")
            peft_config.target_modules = TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING[model_config["model_type"]]
        if peft_config.feedforward_modules is None:
            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING:
                raise ValueError("Please specify `feedforward_modules` in `peft_config`")
            peft_config.feedforward_modules = TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING[
                model_config["model_type"]
            ]
        return peft_config

    def _unload_and_optionally_merge(
        self, merge: bool = True, safe_merge: bool = False, adapter_names: Optional[List[str]] = None
    ):
        r"""
        This method merges the (IA)^3 layers into the base model. This is needed if someone wants to use the base model
        as a standalone model.

        Args:
            safe_merge (`bool`, `optional`, defaults to `False`):
                If True, the merge operation will be performed in a copy of the original weights and check for NaNs
                before merging the weights. This is useful if you want to check if the merge operation will produce
                NaNs. Defaults to `False`.
            adapter_names (`List[str]`, *optional*):
                The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults
                to `None`.
        """
        if getattr(self.model, "is_loaded_in_8bit", False):
            raise ValueError("Cannot merge ia3 layers when the model is loaded in 8-bit mode")

        if getattr(self.model, "is_loaded_in_4bit", False):
            raise ValueError("Cannot merge ia3 layers when the model is loaded in 4-bit mode")

        self._unloading_checks(adapter_names)
        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
        for key in key_list:
            try:
                parent, target, target_name = _get_submodules(self.model, key)
            except AttributeError:
                continue

            if hasattr(target, "base_layer"):
                if merge:
                    target.merge(safe_merge=safe_merge, adapter_names=adapter_names)
                self._replace_module(parent, target_name, target.get_base_layer(), target)
            elif isinstance(target, ModulesToSaveWrapper):
                # save any additional trainable modules part of `modules_to_save`
                setattr(parent, target_name, target.modules_to_save[target.active_adapter])

        return self.model

    def merge_and_unload(self, safe_merge: bool = False, adapter_names: Optional[List[str]] = None) -> torch.nn.Module:
        r"""
        This method merges the IA³ layers into the base model. This is needed if someone wants to use the base model as
        a standalone model.

        Args:
            safe_merge (`bool`):
                whether to activate the safe merging check to check if there is any potential Nan in the adapter
                weights
            adapter_names (`List[str]`, *optional*):
                The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults
                to `None`.

        Example:

        ```py
        >>> from transformers import AutoModelForCausalLM
        >>> from peft import PeftModel

        >>> base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-40b")
        >>> peft_model_id = "smangrul/falcon-40B-int4-peft-lora-sfttrainer-sample"
        >>> model = PeftModel.from_pretrained(base_model, peft_model_id)
        >>> merged_model = model.merge_and_unload()
        ```
        """
        return self._unload_and_optionally_merge(safe_merge=safe_merge, adapter_names=adapter_names)

    def unload(self) -> torch.nn.Module:
        """
        Gets back the base model by removing all the IA³ modules without merging. This gives back the original base
        model.
        """
        return self._unload_and_optionally_merge(merge=False)

    def delete_adapter(self, adapter_name: str) -> None:
        """
        Deletes an existing adapter.

        Args:
            adapter_name (str): Name of the adapter to be deleted.
        """
        if adapter_name not in self.peft_config:
            raise ValueError(f"Adapter {adapter_name} does not exist")
        del self.peft_config[adapter_name]

        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
        new_adapter = None
        for key in key_list:
            _, target, _ = _get_submodules(self.model, key)
            if isinstance(target, IA3Layer):
                target.delete_adapter(adapter_name)
                if new_adapter is None:
                    new_adapter = target.active_adapters[:]

        self.active_adapter = new_adapter or []
