# 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 warnings
from dataclasses import asdict
from enum import Enum
from typing import Optional

import torch
from torch import nn
from tqdm import tqdm

from peft.import_utils import is_bnb_4bit_available, is_bnb_available
from peft.tuners.tuners_utils import (
    BaseTuner,
    BaseTunerLayer,
    check_target_module_exists,
    onload_layer,
)
from peft.utils import (
    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
    ModulesToSaveWrapper,
    _get_submodules,
    get_quantization_config,
)

from .aqlm import dispatch_aqlm
from .awq import dispatch_awq
from .config import OFTConfig
from .eetq import dispatch_eetq
from .gptq import dispatch_gptq
from .hqq import dispatch_hqq
from .inc import dispatch_inc
from .layer import OFTLayer, dispatch_default


class OFTModel(BaseTuner):
    """
    Creates Orthogonal Finetuning model from a pretrained model. The method is described in
    https://huggingface.co/papers/2306.07280

    Args:
        model (`torch.nn.Module`): The model to which the adapter tuner layers will be attached.
        config ([`OFTConfig`]): The configuration of the OFT model.
        adapter_name (`str`): The name of the adapter, defaults to `"default"`.
        low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
            Create empty adapter weights on meta device. Useful to speed up the loading process.

    Returns:
        `torch.nn.Module`: The OFT model.

    Example:
        ```py
        >>> from diffusers import StableDiffusionPipeline
        >>> from peft import OFTModel, OFTConfig

        >>> config_te = OFTConfig(
        ...     r=8,
        ...     target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
        ...     module_dropout=0.0,
        ...     init_weights=True,
        ... )
        >>> config_unet = OFTConfig(
        ...     r=8,
        ...     target_modules=[
        ...         "proj_in",
        ...         "proj_out",
        ...         "to_k",
        ...         "to_q",
        ...         "to_v",
        ...         "to_out.0",
        ...         "ff.net.0.proj",
        ...         "ff.net.2",
        ...     ],
        ...     module_dropout=0.0,
        ...     init_weights=True,
        ... )

        >>> model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
        >>> model.text_encoder = OFTModel(model.text_encoder, config_te, "default")
        >>> model.unet = OFTModel(model.unet, config_unet, "default")
        ```

    **Attributes**:
        - **model** ([`~torch.nn.Module`]) -- The model to be adapted.
        - **peft_config** ([`OFTConfig`]): The configuration of the OFT model.
    """

    prefix: str = "oft_"

    def _check_new_adapter_config(self, config: OFTConfig) -> None:
        """
        A helper method to check the config when a new adapter is being added.

        Raise a ValueError if there is something wrong with the config or if it conflicts with existing adapters.

        """
        # TODO: there should be a check if any of the existing adapters actually has bias != "none", or else the check
        # does not fully correspond to the error message.
        if (len(self.peft_config) > 1) and (config.bias != "none"):
            raise ValueError(
                f"{self.__class__.__name__} supports only 1 adapter with bias. When using multiple adapters, "
                "set bias to 'none' for all adapters."
            )

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

    def _create_and_replace(
        self,
        oft_config,
        adapter_name,
        target,
        target_name,
        parent,
        current_key,
        **optional_kwargs,
    ):
        if current_key is None:
            raise ValueError("Current Key shouldn't be `None`")

        kwargs = {
            "r": oft_config.r,
            "oft_block_size": oft_config.oft_block_size,
            "module_dropout": oft_config.module_dropout,
            "coft": oft_config.coft,
            "eps": oft_config.eps,
            "block_share": oft_config.block_share,
            "use_cayley_neumann": oft_config.use_cayley_neumann,
            "num_cayley_neumann_terms": oft_config.num_cayley_neumann_terms,
            "fan_in_fan_out": oft_config.fan_in_fan_out,
            "init_weights": oft_config.init_weights,
            "loaded_in_8bit": getattr(self.model, "is_loaded_in_8bit", False),
            "loaded_in_4bit": getattr(self.model, "is_loaded_in_4bit", False),
        }

        quant_methods = ["gptq", "aqlm", "awq"]
        for quant_method in quant_methods:
            quantization_config = get_quantization_config(self.model, method=quant_method)
            if quantization_config is not None:
                kwargs[f"{quant_method}_quantization_config"] = quantization_config

        # If it is not a OFTLayer, create a new module, else update it with new adapters
        if not isinstance(target, OFTLayer):
            device_map = self.model.hf_device_map if hasattr(self.model, "hf_device_map") else None
            new_module = self._create_new_module(oft_config, adapter_name, target, device_map=device_map, **kwargs)
            if adapter_name not in self.active_adapters:
                # adding an additional adapter: it is not automatically trainable
                new_module.requires_grad_(False)
            self._replace_module(parent, target_name, new_module, target)
        else:
            target.update_layer(
                adapter_name,
                r=oft_config.r,
                oft_block_size=oft_config.oft_block_size,
                module_dropout=oft_config.module_dropout,
                coft=oft_config.coft,
                eps=oft_config.eps,
                block_share=oft_config.block_share,
                use_cayley_neumann=oft_config.use_cayley_neumann,
                num_cayley_neumann_terms=oft_config.num_cayley_neumann_terms,
                init_weights=oft_config.init_weights,
            )

    def _replace_module(self, parent, child_name, new_module, child):
        setattr(parent, child_name, new_module)
        # It's not necessary to set requires_grad here, as that is handled by
        # _mark_only_adapters_as_trainable

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

        meta = torch.device("meta")
        # dispatch to correct device
        for name, module in new_module.named_modules():
            if (self.prefix in name) or ("ranknum" in name):
                if hasattr(child, "qweight"):
                    weight = child.qweight
                elif hasattr(child, "W_q"):
                    weight = child.W_q
                elif hasattr(child, "weight"):
                    weight = child.weight
                elif getattr(child, "in_proj_weight", None) is not None:  # MHA
                    weight = child.in_proj_weight
                else:
                    weight = next(child.parameters())
                if not any(p.device == meta for p in module.parameters()):
                    module.to(weight.device)

    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

        for active_adapter in self.active_adapters:
            bias = self.peft_config[active_adapter].bias
            if bias == "none":
                continue

            if bias == "all":
                for n, p in model.named_parameters():
                    if "bias" in n:
                        p.requires_grad = True
            elif bias == "oft_only":
                for name, m in model.named_modules():
                    if isinstance(m, OFTLayer) and hasattr(m, "bias") and m.bias is not None:
                        m.bias.requires_grad = True
            else:
                raise NotImplementedError(f"Requested bias: {bias}, is not implemented.")

    @staticmethod
    def _create_new_module(oft_config, adapter_name, target, **kwargs):
        # Collect dispatcher functions to decide what backend to use for the replaced OFT layer. The order matters,
        # because the first match is always used. Therefore, the default layers should be checked last.
        dispatchers = []

        # avoid eager bnb import
        if is_bnb_available():
            from .bnb import dispatch_bnb_8bit

            dispatchers.append(dispatch_bnb_8bit)

        if is_bnb_4bit_available():
            from .bnb import dispatch_bnb_4bit

            dispatchers.append(dispatch_bnb_4bit)

        dispatchers.extend(
            [
                dispatch_eetq,
                dispatch_aqlm,
                dispatch_awq,
                dispatch_gptq,
                dispatch_hqq,
                dispatch_inc,
                dispatch_default,
            ]
        )

        new_module = None
        for dispatcher in dispatchers:
            new_module = dispatcher(target, adapter_name, oft_config=oft_config, **kwargs)
            if new_module is not None:  # first match wins
                break

        if new_module is None:
            # no module could be matched
            raise ValueError(
                f"Target module {target} is not supported. Currently, only the following modules are supported: "
                "`torch.nn.Linear`, `torch.nn.Conv2d`."
            )

        return new_module

    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:
            if name == "model":  # see #1892: prevent infinite recursion if class is not initialized
                raise
            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, (BaseTunerLayer, 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):
        for active_adapter in self.active_adapters:
            val = self.peft_config[active_adapter].bias
            if val != "none":
                msg = (
                    f"Careful, disabling adapter layers with bias configured to be '{val}' does not produce the same "
                    "output as the base model would without adaption."
                )
                warnings.warn(msg)
        self._set_adapter_layers(enabled=False)

    def set_adapter(self, adapter_name, inference_mode: bool = False):
        """Set the active adapter(s).

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

            adapter_name (`str` or `list[str]`):
                Name(s) of the adapter(s) to be activated.
            inference_mode (bool, optional):
                 Whether the activated adapter should be frozen (i.e. `requires_grad=False`). Default is False.
        """
        self.set_auxiliary_adapters(adapter_name, inference_mode=inference_mode)
        for module in self.model.modules():
            if isinstance(module, OFTLayer):
                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, inference_mode=inference_mode)
        self.active_adapter = adapter_name

    def _check_merge_allowed(self):
        """Verify that the configuration supports merging.

        Currently gptq quantization and replicated layers do not support merging.
        """
        super()._check_merge_allowed()
        if getattr(self.model, "quantization_method", None) == "gptq":
            raise ValueError("Cannot merge OFT layers when the model is gptq quantized")
        if self.peft_config.get("layer_replication"):
            raise ValueError("Cannot merge OFT layers when base model layers are replicated")

    @staticmethod
    def _prepare_adapter_config(peft_config, model_config):
        if peft_config.target_modules is None:
            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING:
                raise ValueError("Please specify `target_modules` in `peft_config`")
            peft_config.target_modules = set(
                TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]]
            )
        return peft_config

    def _unload_and_optionally_merge(
        self,
        merge=True,
        progressbar: bool = False,
        safe_merge: bool = False,
        adapter_names: Optional[list[str]] = None,
    ):
        if merge:
            self._check_merge_allowed()

        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
        desc = "Unloading " + ("and merging " if merge else "") + "model"
        for key in tqdm(key_list, disable=not progressbar, desc=desc):
            try:
                parent, target, target_name = _get_submodules(self.model, key)
            except AttributeError:
                continue
            with onload_layer(target):
                if hasattr(target, "unload_and_optionally_merge_module"):
                    # if layers have special unloading method, like MultiheadAttention, use that
                    unloaded_module = target.unload_and_optionally_merge_module(
                        merge=merge, safe_merge=safe_merge, adapter_names=adapter_names
                    )
                    self._replace_module(parent, target_name, unloaded_module, target)
                elif 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)

        return self.model

    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 list(self.peft_config.keys()):
            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, OFTLayer):
                target.delete_adapter(adapter_name)
                if new_adapter is None:
                    new_adapter = target.active_adapters[:]

        self.active_adapter = new_adapter or []
        self._delete_auxiliary_adapter(adapter_name, new_active_adapters=new_adapter)

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

        Args:
            progressbar (`bool`):
                whether to show a progressbar indicating the unload and merge process
            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`.

        """
        return self._unload_and_optionally_merge(
            progressbar=progressbar, safe_merge=safe_merge, adapter_names=adapter_names
        )

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