# 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
#
# 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.
import re
import warnings
from dataclasses import asdict, dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.pytorch_utils import Conv1D

from ..import_utils import is_bnb_available
from ..utils import (
    TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING,
    TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING,
    ModulesToSaveWrapper,
    PeftConfig,
    PeftType,
    _freeze_adapter,
    _get_submodules,
    transpose,
)


if is_bnb_available():
    import bitsandbytes as bnb


@dataclass
class IA3Config(PeftConfig):
    """
    This is the configuration class to store the configuration of a [`IA3Model`].

    Args:
        target_modules (`Union[List[str],str]`): The names of the modules to apply (IA)^3 to.
        feedforward_modules (`Union[List[str],str]`): The names of the modules to be treated as feedforward modules
        as in the original paper.
        fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out).
        For example, gpt-2 uses `Conv1D` which stores weights like (fan_in, fan_out) and hence this should be set to `True`.:
        modules_to_save (`List[str]`):List of modules apart from (IA)^3 layers to be set as trainable
            and saved in the final checkpoint.
        init_ia3_weights (`bool`): Whether to initialize the vectors in the (IA)^3 layers, defaults to `True`.
    """

    target_modules: Optional[Union[List[str], str]] = field(
        default=None,
        metadata={
            "help": "List of module names or regex expression of the module names to replace with ia3."
            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
        },
    )
    feedforward_modules: Optional[Union[List[str], str]] = field(
        default=None,
        metadata={
            "help": "List of module names or a regex expression of module names which are feedforward"
            "For example, ['output.dense']"
        },
    )
    fan_in_fan_out: bool = field(
        default=False,
        metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"},
    )
    modules_to_save: Optional[List[str]] = field(
        default=None,
        metadata={
            "help": "List of modules apart from (IA)^3 layers to be set as trainable and saved in the final checkpoint. "
            "For example, in Sequence Classification or Token Classification tasks, "
            "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
        },
    )
    init_ia3_weights: bool = field(
        default=True,
        metadata={"help": "Whether to initialize the vectors in the (IA)^3 layers."},
    )

    def __post_init__(self):
        self.peft_type = PeftType.IA3


class IA3Model(torch.nn.Module):
    """
    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.

    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.
    """

    def __init__(self, model, config, adapter_name):
        super().__init__()
        self.model = model
        self.forward = self.model.forward
        self.peft_config = config
        self.add_adapter(adapter_name, self.peft_config[adapter_name])

    def add_adapter(self, adapter_name, config=None):
        if config is not None:
            model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
            config = self._prepare_ia3_config(config, model_config)
            self.peft_config[adapter_name] = config
        self._find_and_replace(adapter_name)

        mark_only_ia3_as_trainable(self.model)
        if self.peft_config[adapter_name].inference_mode:
            _freeze_adapter(self.model, adapter_name)

    def _check_quantization_dependency(self):
        loaded_in_4bit = getattr(self.model, "is_loaded_in_4bit", False)
        if loaded_in_4bit:
            raise NotImplementedError(
                "4-bit quantization is not supported for IA3 yet, 8-bit quantization can be used instead."
            )
        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
        if loaded_in_8bit and not is_bnb_available():
            raise ImportError(
                "To use (IA)^3 with 8-bit quantization, please install the `bitsandbytes` package. "
                "You can install it with `pip install bitsandbytes`."
            )

    def _create_new_module(self, ia3_config, adapter_name, target, is_feedforward):
        kwargs = {
            "fan_in_fan_out": ia3_config.fan_in_fan_out,
            "init_ia3_weights": ia3_config.init_ia3_weights,
        }
        bias = hasattr(target, "bias") and target.bias is not None
        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)

        if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
            eightbit_kwargs = kwargs.copy()
            eightbit_kwargs.update(
                {
                    "has_fp16_weights": target.state.has_fp16_weights,
                    "memory_efficient_backward": target.state.memory_efficient_backward,
                    "threshold": target.state.threshold,
                    "index": target.index,
                }
            )
            new_module = Linear8bitLt(
                adapter_name,
                target.in_features,
                target.out_features,
                is_feedforward,
                bias=bias,
                **eightbit_kwargs,
            )
        else:
            #  Create a new Linear module with (IA)^3 parameters for torch.nn.Linear
            # or Conv1D modules
            if isinstance(target, torch.nn.Linear):
                in_features, out_features = target.in_features, target.out_features
                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
            elif isinstance(target, Conv1D):
                in_features, out_features = (
                    target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
                )
                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
            else:
                raise ValueError(
                    f"Target module {target} is not supported. "
                    f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
                )
            new_module = Linear(
                adapter_name, in_features, out_features, is_feedforward=is_feedforward, bias=bias, **kwargs
            )
        return new_module

    def _check_target_module_exists(self, ia3_config, key):
        if isinstance(ia3_config.target_modules, str):
            target_module_found = re.fullmatch(ia3_config.target_modules, key)
        else:
            target_module_found = any(
                self._is_valid_match(key, target_key) for target_key in ia3_config.target_modules
            )
        return target_module_found

    def _find_and_replace(self, adapter_name):
        ia3_config = self.peft_config[adapter_name]
        if not ia3_config.feedforward_modules:
            ia3_config.feedforward_modules = []  # convert to list if None
        self._check_quantization_dependency()
        is_target_modules_in_base_model = False

        key_list = [key for key, _ in self.model.named_modules()]
        for key in key_list:
            if not self._check_target_module_exists(ia3_config, key):
                continue
            # check if target module is in feedforward_modules
            if isinstance(ia3_config.feedforward_modules, str):
                is_feedforward = re.fullmatch(ia3_config.feedforward_modules, key)
            else:
                is_feedforward = any(key.endswith(target_key) for target_key in ia3_config.feedforward_modules)

            if not is_target_modules_in_base_model:
                is_target_modules_in_base_model = True
            parent, target, target_name = _get_submodules(self.model, key)

            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, is_feedforward)
                self._replace_module(parent, target_name, new_module, target)
        if not is_target_modules_in_base_model:
            raise ValueError(
                f"Target modules {ia3_config.target_modules} not found in the base model. "
                f"Please check the target modules and try again."
            )

    @staticmethod
    def _is_valid_match(key: str, target_key: str):
        """
        Helper function to match module names target_key and key. Makes sure that either the key is exactly the
        target_key or the target_key is a submodule of key
        """
        if key.endswith(target_key):
            if len(key) > len(target_key):
                return key.endswith("." + target_key)  # must be a sub module
            return True
        return False

    def _replace_module(self, parent_module, child_name, new_module, old_module):
        setattr(parent_module, child_name, new_module)
        new_module.weight = old_module.weight
        if old_module.bias is not None:
            new_module.bias = old_module.bias
        if getattr(old_module, "state", None) is not None:
            new_module.state = old_module.state
            new_module.to(old_module.weight.device)

        # dispatch to correct device
        for name, module in new_module.named_modules():
            if "ia3_" in name:
                module.to(old_module.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):
                module.disable_adapters = False if enabled else True
            elif isinstance(module, ModulesToSaveWrapper):
                module.disable_adapters = False if enabled else True

    def enable_adapter_layers(self):
        self._set_adapter_layers(enabled=True)

    def disable_adapter_layers(self):
        self._set_adapter_layers(enabled=False)

    def set_adapter(self, adapter_name):
        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.active_adapter = adapter_name

    @staticmethod
    def _prepare_ia3_config(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 merge_and_unload(self):
        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.
        """
        if getattr(self.config, "model_type", None) == "gpt2":
            raise ValueError("GPT2 models are not supported for merging ia3 layers")

        if getattr(self.model, "is_loaded_in_8bit", False):
            raise ValueError("Cannot merge ia3 layers when the model is loaded in 8-bit mode")

        key_list = [key for key, _ in self.model.named_modules() if "ia3" not in key]
        for key in key_list:
            try:
                parent, target, target_name = _get_submodules(self.model, key)
            except AttributeError:
                continue
            if isinstance(target, IA3Layer):
                bias = target.bias is not None
                new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)
                target.merge()
                self._replace_module(parent, target_name, new_module, target)

            # save any additional trainable modules part of `modules_to_save`
            if isinstance(target, ModulesToSaveWrapper):
                setattr(parent, target_name, target.modules_to_save[target.active_adapter])

        return self.model


# Below code is based on https://github.com/microsoft/lora/blob/main/loralib/layers.py
# and modified to work with PyTorch FSDP


#  ------------------------------------------------------------------------------------------
#  Copyright (c) Microsoft Corporation. All rights reserved.
#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
#  ------------------------------------------------------------------------------------------


def mark_only_ia3_as_trainable(model: nn.Module) -> None:
    for n, p in model.named_parameters():
        if "ia3_" not in n:
            p.requires_grad = False


class IA3Layer:
    def __init__(
        self,
        in_features: int,
        out_features: int,
        is_feedforward: bool,
    ):
        self.scaling = {}
        self.ia3_l = nn.ParameterDict({})
        # Mark the weight as unmerged
        self.merged = False
        self.disable_adapters = False
        self.in_features = in_features
        self.out_features = out_features
        self.is_feedforward = is_feedforward

    def update_layer(self, adapter_name, init_ia3_weights):
        # Actual trainable parameters
        if self.is_feedforward:
            weight = torch.randn((1, self.in_features))
        else:
            weight = torch.randn((self.out_features, 1))
        self.ia3_l.update(nn.ParameterDict({adapter_name: nn.Parameter(weight)}))
        if init_ia3_weights:
            self.reset_ia3_parameters(adapter_name)
        self.to(self.weight.device)

    def reset_ia3_parameters(self, adapter_name):
        if adapter_name in self.ia3_l.keys():
            # initialize learned vector with torch.ones
            nn.init.constant_(self.ia3_l[adapter_name], 1.0)


class Linear(nn.Linear, IA3Layer):
    # (IA)^3 implemented in a dense layer
    def __init__(
        self,
        adapter_name: str,
        in_features: int,
        out_features: int,
        fan_in_fan_out: bool = False,  # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
        is_feedforward: bool = False,  # Set to True if the layer is treated as a feedforward layer
        **kwargs,
    ):
        init_ia3_weights = kwargs.pop("init_ia3_weights", True)

        nn.Linear.__init__(self, in_features, out_features, **kwargs)
        IA3Layer.__init__(self, in_features=in_features, out_features=out_features, is_feedforward=is_feedforward)
        # Freezing the pre-trained weight matrix
        self.weight.requires_grad = False

        self.fan_in_fan_out = fan_in_fan_out
        if fan_in_fan_out:
            self.weight.data = self.weight.data.T

        nn.Linear.reset_parameters(self)
        self.update_layer(adapter_name, init_ia3_weights)
        self.active_adapter = adapter_name

        self.is_feedforward = is_feedforward

    def merge(self):
        if self.active_adapter not in self.ia3_l.keys():
            return
        if self.merged:
            warnings.warn("Already merged. Nothing to do.")
            return

        self.weight = transpose(self.weight, self.fan_in_fan_out)
        self.weight.data = torch.mul(self.weight.data, self.ia3_l[self.active_adapter].data)
        self.weight = transpose(self.weight, self.fan_in_fan_out)

        self.merged = True

    def unmerge(self):
        if self.active_adapter not in self.ia3_l.keys():
            return
        if not self.merged:
            warnings.warn("Already unmerged. Nothing to do.")
            return

        warnings.warn("Unmerge result can be inaccurate for (IA)^3.")
        self.weight = transpose(self.weight, self.fan_in_fan_out)
        # divide by (IA)^3 vector. Add tolerace to avoid division by zero
        self.weight.data = torch.div(self.weight.data, self.ia3_l[self.active_adapter].data + 1e-8)
        self.weight = transpose(self.weight, self.fan_in_fan_out)

        self.merged = False

    def forward(self, x: torch.Tensor):
        previous_dtype = x.dtype

        if self.active_adapter not in self.ia3_l.keys():
            return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)

        if self.disable_adapters:
            if self.merged:
                self.unmerge()
            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
        elif not self.merged:
            if self.is_feedforward:
                x = x.to(self.ia3_l[self.active_adapter].dtype)
                interm = x * self.ia3_l[self.active_adapter].flatten()
                result = F.linear(
                    interm.to(self.weight.dtype),
                    transpose(self.weight, self.fan_in_fan_out),
                    bias=self.bias,
                )
            else:
                result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
                result = result.to(self.ia3_l[self.active_adapter].dtype) * self.ia3_l[self.active_adapter].flatten()
        else:
            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)

        result = result.to(previous_dtype)

        return result


if is_bnb_available():

    class Linear8bitLt(bnb.nn.Linear8bitLt, IA3Layer):
        # (IA)^3 implemented in a dense layer
        def __init__(
            self,
            adapter_name,
            in_features,
            out_features,
            is_feedforward,
            **kwargs,
        ):
            bnb.nn.Linear8bitLt.__init__(
                self,
                in_features,
                out_features,
                bias=kwargs.get("bias", True),
                has_fp16_weights=kwargs.get("has_fp16_weights", True),
                memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
                threshold=kwargs.get("threshold", 0.0),
                index=kwargs.get("index", None),
            )
            IA3Layer.__init__(self, in_features=in_features, out_features=out_features, is_feedforward=is_feedforward)

            # Freezing the pre-trained weight matrix
            self.weight.requires_grad = False

            init_ia3_weights = kwargs.pop("init_ia3_weights", True)
            self.update_layer(adapter_name, init_ia3_weights)
            self.active_adapter = adapter_name
            self.is_feedforward = is_feedforward

        def forward(self, x: torch.Tensor):
            if self.disable_adapters or self.active_adapter not in self.ia3_l.keys():
                return super().forward(x)
            else:
                if not torch.is_autocast_enabled():
                    if x.dtype != torch.float32:
                        x = x.float()
                    if self.is_feedforward:
                        result = super().forward(x * self.ia3_l[self.active_adapter].flatten())
                    else:
                        result = super().forward(x)
                        expected_dtype = result.dtype
                        result = (result * self.ia3_l[self.active_adapter].flatten()).to(expected_dtype)
                else:
                    if self.is_feedforward:
                        result = super().forward(x * self.ia3_l[self.active_adapter].flatten())
                    else:
                        result = result * self.ia3_l[self.active_adapter].flatten()
            return result
