# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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 copy
import inspect
import os
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union

import safetensors
from huggingface_hub import model_info
from huggingface_hub.constants import HF_HUB_OFFLINE
from huggingface_hub.utils import validate_hf_hub_args

import mindspore as ms
from mindspore import nn

from townwish_mindone_testing.safetensors.mindspore import load_file, save_file
from townwish_mindone_testing.transformers import CLIPTextModel, CLIPTextModelWithProjection, MSPreTrainedModel

from ..utils import (
    _get_model_file,
    convert_state_dict_to_diffusers,
    convert_state_dict_to_peft,
    delete_adapter_layers,
    get_adapter_name,
    get_peft_kwargs,
    is_peft_version,
    logging,
    recurse_remove_peft_layers,
    scale_lora_layers,
    set_adapter_layers,
    set_weights_and_activate_adapters,
)
from .lora_conversion_utils import _convert_non_diffusers_lora_to_diffusers, _maybe_map_sgm_blocks_to_diffusers

logger = logging.get_logger(__name__)

TEXT_ENCODER_NAME = "text_encoder"
UNET_NAME = "unet"
TRANSFORMER_NAME = "transformer"

LORA_WEIGHT_NAME = "pytorch_lora_weights.ckpt"
LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"

LORA_DEPRECATION_MESSAGE = "You are using an old version of LoRA backend. This will be deprecated in the next releases in favor of PEFT make sure to install the latest PEFT and transformers packages in the future."  # noqa: E501


def text_encoder_attn_modules(text_encoder):
    attn_modules = []

    if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
        for i, layer in enumerate(text_encoder.text_model.encoder.layers):
            name = f"text_model.encoder.layers.{i}.self_attn"
            mod = layer.self_attn
            attn_modules.append((name, mod))
    else:
        raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}")

    return attn_modules


def text_encoder_mlp_modules(text_encoder):
    mlp_modules = []

    if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
        for i, layer in enumerate(text_encoder.text_model.encoder.layers):
            mlp_mod = layer.mlp
            name = f"text_model.encoder.layers.{i}.mlp"
            mlp_modules.append((name, mlp_mod))
    else:
        raise ValueError(f"do not know how to get mlp modules for: {text_encoder.__class__.__name__}")

    return mlp_modules


class LoraLoaderMixin:
    r"""
    Load LoRA layers into [`UNet2DConditionModel`] and
    [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
    """

    text_encoder_name = TEXT_ENCODER_NAME
    unet_name = UNET_NAME
    transformer_name = TRANSFORMER_NAME
    num_fused_loras = 0

    def load_lora_weights(
        self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, ms.Tensor]], adapter_name=None, **kwargs
    ):
        """
        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
        `self.text_encoder`.

        All kwargs are forwarded to `self.lora_state_dict`.

        See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.

        See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
        `self.unet`.

        See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
        into `self.text_encoder`.

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
            kwargs (`dict`, *optional*):
                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
        """
        # if a dict is passed, copy it instead of modifying it inplace
        if isinstance(pretrained_model_name_or_path_or_dict, dict):
            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()

        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
        state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)

        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
        if not is_correct_format:
            raise ValueError("Invalid LoRA checkpoint.")

        self.load_lora_into_unet(
            state_dict,
            network_alphas=network_alphas,
            unet=getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet,
            adapter_name=adapter_name,
            _pipeline=self,
        )
        self.load_lora_into_text_encoder(
            state_dict,
            network_alphas=network_alphas,
            text_encoder=getattr(self, self.text_encoder_name)
            if not hasattr(self, "text_encoder")
            else self.text_encoder,
            lora_scale=self.lora_scale,
            adapter_name=adapter_name,
            _pipeline=self,
        )

    @classmethod
    @validate_hf_hub_args
    def lora_state_dict(
        cls,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, ms.Tensor]],
        **kwargs,
    ):
        r"""
        Return state dict for lora weights and the network alphas.

        <Tip warning={true}>

        We support loading A1111 formatted LoRA checkpoints in a limited capacity.

        This function is experimental and might change in the future.

        </Tip>

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                Can be either:

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      with [`ModelMixin.save_pretrained`].
                    - A [torch state
                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download:
                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1
                of Diffusers.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only (`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            subfolder (`str`, *optional*, defaults to `""`):
                The subfolder location of a model file within a larger model repository on the Hub or locally.
            weight_name (`str`, *optional*, defaults to None):
                Name of the serialized state dict file.
        """
        # Load the main state dict first which has the LoRA layers for either of
        # UNet and text encoder or both.
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", None)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)
        weight_name = kwargs.pop("weight_name", None)
        unet_config = kwargs.pop("unet_config", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = True
            allow_pickle = True

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }

        model_file = None
        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
            # Let's first try to load .safetensors weights
            if (use_safetensors and weight_name is None) or (
                weight_name is not None and weight_name.endswith(".safetensors")
            ):
                try:
                    # Here we're relaxing the loading check to enable more Inference API
                    # friendliness where sometimes, it's not at all possible to automatically
                    # determine `weight_name`.
                    if weight_name is None:
                        weight_name = cls._best_guess_weight_name(
                            pretrained_model_name_or_path_or_dict,
                            file_extension=".safetensors",
                            local_files_only=local_files_only,
                        )
                    model_file = _get_model_file(
                        pretrained_model_name_or_path_or_dict,
                        weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=resume_download,
                        proxies=proxies,
                        local_files_only=local_files_only,
                        token=token,
                        revision=revision,
                        subfolder=subfolder,
                        user_agent=user_agent,
                    )
                    state_dict = load_file(model_file)
                except (IOError, safetensors.SafetensorError) as e:
                    if not allow_pickle:
                        raise e
                    # try loading non-safetensors weights
                    model_file = None
                    pass

            if model_file is None:
                if weight_name is None:
                    weight_name = cls._best_guess_weight_name(
                        pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
                    )
                model_file = _get_model_file(
                    pretrained_model_name_or_path_or_dict,
                    weights_name=weight_name or LORA_WEIGHT_NAME,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                )
                raise NotImplementedError(
                    f"Only supports deserialization of weights file in safetensors format, but got {model_file}"
                )
        else:
            state_dict = pretrained_model_name_or_path_or_dict

        network_alphas = None
        # TODO: replace it with a method from `state_dict_utils`
        if all(
            (
                k.startswith("lora_te_")
                or k.startswith("lora_unet_")
                or k.startswith("lora_te1_")
                or k.startswith("lora_te2_")
            )
            for k in state_dict.keys()
        ):
            # Map SDXL blocks correctly.
            if unet_config is not None:
                # use unet config to remap block numbers
                state_dict = _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config)
            state_dict, network_alphas = _convert_non_diffusers_lora_to_diffusers(state_dict)

        return state_dict, network_alphas

    @classmethod
    def _best_guess_weight_name(
        cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
    ):
        if local_files_only or HF_HUB_OFFLINE:
            raise ValueError("When using the offline mode, you must specify a `weight_name`.")

        targeted_files = []

        if os.path.isfile(pretrained_model_name_or_path_or_dict):
            return
        elif os.path.isdir(pretrained_model_name_or_path_or_dict):
            targeted_files = [
                f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)
            ]
        else:
            files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
            targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
        if len(targeted_files) == 0:
            return

        # "scheduler" does not correspond to a LoRA checkpoint.
        # "optimizer" does not correspond to a LoRA checkpoint
        # only top-level checkpoints are considered and not the other ones, hence "checkpoint".
        unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
        targeted_files = list(
            filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
        )

        if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
        elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))

        if len(targeted_files) > 1:
            raise ValueError(
                f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."  # noqa: E501
            )
        weight_name = targeted_files[0]
        return weight_name

    @classmethod
    def load_lora_into_unet(cls, state_dict, network_alphas, unet, adapter_name=None, _pipeline=None):
        """
        This will load the LoRA layers specified in `state_dict` into `unet`.

        Parameters:
            state_dict (`dict`):
                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
                encoder lora layers.
            network_alphas (`Dict[str, float]`):
                The value of the network alpha used for stable learning and preventing underflow. This value has the
                same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
                link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
            unet (`UNet2DConditionModel`):
                The UNet model to load the LoRA layers into.
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
        """

        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
        # then the `state_dict` keys should have `cls.unet_name` and/or `cls.text_encoder_name` as
        # their prefixes.
        keys = list(state_dict.keys())
        only_text_encoder = all(key.startswith(cls.text_encoder_name) for key in keys)
        if not only_text_encoder:
            # Load the layers corresponding to UNet.
            logger.info(f"Loading {cls.unet_name}.")
            unet.load_attn_procs(
                state_dict, network_alphas=network_alphas, adapter_name=adapter_name, _pipeline=_pipeline
            )

    @classmethod
    def load_lora_into_text_encoder(
        cls,
        state_dict,
        network_alphas,
        text_encoder,
        prefix=None,
        lora_scale=1.0,
        adapter_name=None,
        _pipeline=None,
    ):
        """
        This will load the LoRA layers specified in `state_dict` into `text_encoder`

        Parameters:
            state_dict (`dict`):
                A standard state dict containing the lora layer parameters. The key should be prefixed with an
                additional `text_encoder` to distinguish between unet lora layers.
            network_alphas (`Dict[str, float]`):
                See `LoRALinearLayer` for more details.
            text_encoder (`CLIPTextModel`):
                The text encoder model to load the LoRA layers into.
            prefix (`str`):
                Expected prefix of the `text_encoder` in the `state_dict`.
            lora_scale (`float`):
                How much to scale the output of the lora linear layer before it is added with the output of the regular
                lora layer.
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
        """
        from townwish_mindone_testing.diffusers._peft import LoraConfig

        # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
        # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
        # their prefixes.
        keys = list(state_dict.keys())
        prefix = cls.text_encoder_name if prefix is None else prefix

        # Safe prefix to check with.
        if any(cls.text_encoder_name in key for key in keys):
            # Load the layers corresponding to text encoder and make necessary adjustments.
            text_encoder_keys = [k for k in keys if k.startswith(prefix) and k.split(".")[0] == prefix]
            text_encoder_lora_state_dict = {
                k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in text_encoder_keys
            }

            if len(text_encoder_lora_state_dict) > 0:
                logger.info(f"Loading {prefix}.")
                rank = {}
                text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)

                # convert state dict
                text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)

                for name, _ in text_encoder_attn_modules(text_encoder):
                    rank_key = f"{name}.out_proj.lora_B.weight"
                    rank[rank_key] = text_encoder_lora_state_dict[rank_key].shape[1]

                patch_mlp = any(".mlp." in key for key in text_encoder_lora_state_dict.keys())
                if patch_mlp:
                    for name, _ in text_encoder_mlp_modules(text_encoder):
                        rank_key_fc1 = f"{name}.fc1.lora_B.weight"
                        rank_key_fc2 = f"{name}.fc2.lora_B.weight"

                        rank[rank_key_fc1] = text_encoder_lora_state_dict[rank_key_fc1].shape[1]
                        rank[rank_key_fc2] = text_encoder_lora_state_dict[rank_key_fc2].shape[1]

                if network_alphas is not None:
                    alpha_keys = [
                        k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix
                    ]
                    network_alphas = {
                        k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                    }

                lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict, is_unet=False)
                if "use_dora" in lora_config_kwargs:
                    if lora_config_kwargs["use_dora"]:
                        if is_peft_version("<", "0.9.0"):
                            raise ValueError(
                                "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
                            )
                    else:
                        if is_peft_version("<", "0.9.0"):
                            lora_config_kwargs.pop("use_dora")
                lora_config = LoraConfig(**lora_config_kwargs)

                # adapter_name
                if adapter_name is None:
                    adapter_name = get_adapter_name(text_encoder)

                # inject LoRA layers and load the state dict
                # in transformers we automatically check whether the adapter name is already in use or not
                text_encoder.load_adapter(
                    adapter_name=adapter_name,
                    adapter_state_dict=text_encoder_lora_state_dict,
                    peft_config=lora_config,
                )

                # scale LoRA layers with `lora_scale`
                scale_lora_layers(text_encoder, weight=lora_scale)

                text_encoder.to(dtype=text_encoder.dtype)

    @classmethod
    def load_lora_into_transformer(cls, state_dict, network_alphas, transformer, adapter_name=None, _pipeline=None):
        """
        This will load the LoRA layers specified in `state_dict` into `transformer`.

        Parameters:
            state_dict (`dict`):
                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
                encoder lora layers.
            network_alphas (`Dict[str, float]`):
                See `LoRALinearLayer` for more details.
            unet (`UNet2DConditionModel`):
                The UNet model to load the LoRA layers into.
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
        """
        from townwish_mindone_testing.diffusers._peft import (
            LoraConfig,
            inject_adapter_in_model,
            set_peft_model_state_dict,
        )

        keys = list(state_dict.keys())

        transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
        state_dict = {
            k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
        }

        if network_alphas is not None:
            alpha_keys = [k for k in network_alphas.keys() if k.startswith(cls.transformer_name)]
            network_alphas = {
                k.replace(f"{cls.transformer_name}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
            }

        if len(state_dict.keys()) > 0:
            if adapter_name in getattr(transformer, "peft_config", {}):
                raise ValueError(
                    f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
                )

            rank = {}
            for key, val in state_dict.items():
                if "lora_B" in key:
                    rank[key] = val.shape[1]

            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict)
            if "use_dora" in lora_config_kwargs:
                if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                    raise ValueError(
                        "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
                    )
                else:
                    lora_config_kwargs.pop("use_dora")
            lora_config = LoraConfig(**lora_config_kwargs)

            # adapter_name
            if adapter_name is None:
                adapter_name = get_adapter_name(transformer)

            inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
            incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)

            if incompatible_keys is not None:
                # check only for unexpected keys
                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                if unexpected_keys:
                    logger.warning(
                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
                        f" {unexpected_keys}. "
                    )

    @property
    def lora_scale(self) -> float:
        # property function that returns the lora scale which can be set at run time by the pipeline.
        # if _lora_scale has not been set, return 1
        return self._lora_scale if hasattr(self, "_lora_scale") else 1.0

    def _remove_text_encoder_monkey_patch(self):
        remove_method = recurse_remove_peft_layers
        if hasattr(self, "text_encoder"):
            remove_method(self.text_encoder)
            # In case text encoder have no Lora attached
            if getattr(self.text_encoder, "peft_config", None) is not None:
                del self.text_encoder.peft_config
                self.text_encoder._hf_peft_config_loaded = None

        if hasattr(self, "text_encoder_2"):
            remove_method(self.text_encoder_2)
            if getattr(self.text_encoder_2, "peft_config", None) is not None:
                del self.text_encoder_2.peft_config
                self.text_encoder_2._hf_peft_config_loaded = None

    @classmethod
    def save_lora_weights(
        cls,
        save_directory: Union[str, os.PathLike],
        unet_lora_layers: Dict[str, Union[nn.Cell, ms.Tensor]] = None,
        text_encoder_lora_layers: Dict[str, nn.Cell] = None,
        transformer_lora_layers: Dict[str, nn.Cell] = None,
        is_main_process: bool = True,
        weight_name: str = None,
        save_function: Callable = None,
        safe_serialization: bool = True,
    ):
        r"""
        Save the LoRA parameters corresponding to the UNet and text encoder.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to save LoRA parameters to. Will be created if it doesn't exist.
            unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `unet`.
            text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
                encoder LoRA state dict because it comes from 🤗 Transformers.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful during distributed training and you
                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful during distributed training when you need to
                replace `torch.save` with another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `True`):
                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
        """
        state_dict = {}

        def pack_weights(layers, prefix):
            layers_weights = {k: v for k, v in layers.parameters_and_names()} if isinstance(layers, nn.Cell) else layers
            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
            return layers_state_dict

        if not (unet_lora_layers or text_encoder_lora_layers or transformer_lora_layers):
            raise ValueError(
                "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers`, or `transformer_lora_layers`."
            )

        if unet_lora_layers:
            state_dict.update(pack_weights(unet_lora_layers, cls.unet_name))

        if text_encoder_lora_layers:
            state_dict.update(pack_weights(text_encoder_lora_layers, cls.text_encoder_name))

        if transformer_lora_layers:
            state_dict.update(pack_weights(transformer_lora_layers, "transformer"))

        # Save the model
        cls.write_lora_layers(
            state_dict=state_dict,
            save_directory=save_directory,
            is_main_process=is_main_process,
            weight_name=weight_name,
            save_function=save_function,
            safe_serialization=safe_serialization,
        )

    @staticmethod
    def write_lora_layers(
        state_dict: Dict[str, ms.Tensor],
        save_directory: str,
        is_main_process: bool,
        weight_name: str,
        save_function: Callable,
        safe_serialization: bool,
    ):
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        if save_function is None:
            if safe_serialization:

                def save_function(weights, filename):
                    return save_file(weights, filename, metadata={"format": "np"})

            else:
                save_function = ms.save_checkpoint

        os.makedirs(save_directory, exist_ok=True)

        if weight_name is None:
            if safe_serialization:
                weight_name = LORA_WEIGHT_NAME_SAFE
            else:
                weight_name = LORA_WEIGHT_NAME

        save_path = Path(save_directory, weight_name).as_posix()
        save_function(state_dict, save_path)
        logger.info(f"Model weights saved in {save_path}")

    def unload_lora_weights(self):
        """
        Unloads the LoRA parameters.

        Examples:

        ```python
        >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
        >>> pipeline.unload_lora_weights()
        >>> ...
        ```
        """
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        unet.unload_lora()

        # Safe to call the following regardless of LoRA.
        self._remove_text_encoder_monkey_patch()

    def fuse_lora(
        self,
        fuse_unet: bool = True,
        fuse_text_encoder: bool = True,
        lora_scale: float = 1.0,
        safe_fusing: bool = False,
        adapter_names: Optional[List[str]] = None,
    ):
        r"""
        Fuses the LoRA parameters into the original parameters of the corresponding blocks.

        <Tip warning={true}>

        This is an experimental API.

        </Tip>

        Args:
            fuse_unet (`bool`, defaults to `True`): Whether to fuse the UNet LoRA parameters.
            fuse_text_encoder (`bool`, defaults to `True`):
                Whether to fuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
                LoRA parameters then it won't have any effect.
            lora_scale (`float`, defaults to 1.0):
                Controls how much to influence the outputs with the LoRA parameters.
            safe_fusing (`bool`, defaults to `False`):
                Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
            adapter_names (`List[str]`, *optional*):
                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.

        Example:

        ```py
        from diffusers import DiffusionPipeline
        import torch

        pipeline = DiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
        ).to("cuda")
        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
        pipeline.fuse_lora(lora_scale=0.7)
        ```
        """
        from townwish_mindone_testing.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        if fuse_unet or fuse_text_encoder:
            self.num_fused_loras += 1
            if self.num_fused_loras > 1:
                logger.warning(
                    "The current API is supported for operating with a single LoRA file. You are trying to load and fuse more than one LoRA which is not well-supported.",  # noqa: E501
                )

        if fuse_unet:
            unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
            unet.fuse_lora(lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names)

        def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):
            merge_kwargs = {"safe_merge": safe_fusing}

            for _, module in text_encoder.cells_and_names():
                if isinstance(module, BaseTunerLayer):
                    if lora_scale != 1.0:
                        module.scale_layer(lora_scale)

                    # For BC with previous PEFT versions, we need to check the signature
                    # of the `merge` method to see if it supports the `adapter_names` argument.
                    supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
                    if "adapter_names" in supported_merge_kwargs:
                        merge_kwargs["adapter_names"] = adapter_names
                    elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
                        raise RuntimeError("The 'adapter_names' argument is not supported with 'BaseTunerLayer.merge'")

                    module.merge(**merge_kwargs)

        if fuse_text_encoder:
            if hasattr(self, "text_encoder"):
                fuse_text_encoder_lora(self.text_encoder, lora_scale, safe_fusing, adapter_names=adapter_names)
            if hasattr(self, "text_encoder_2"):
                fuse_text_encoder_lora(self.text_encoder_2, lora_scale, safe_fusing, adapter_names=adapter_names)

    def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):
        r"""
        Reverses the effect of
        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).

        <Tip warning={true}>

        This is an experimental API.

        </Tip>

        Args:
            unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
            unfuse_text_encoder (`bool`, defaults to `True`):
                Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
                LoRA parameters then it won't have any effect.
        """
        from townwish_mindone_testing.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        if unfuse_unet:
            for _, module in unet.cells_and_names():
                if isinstance(module, BaseTunerLayer):
                    module.unmerge()

        def unfuse_text_encoder_lora(text_encoder):
            for _, module in text_encoder.cells_and_names():
                if isinstance(module, BaseTunerLayer):
                    module.unmerge()

        if unfuse_text_encoder:
            if hasattr(self, "text_encoder"):
                unfuse_text_encoder_lora(self.text_encoder)
            if hasattr(self, "text_encoder_2"):
                unfuse_text_encoder_lora(self.text_encoder_2)

        self.num_fused_loras -= 1

    def set_adapters_for_text_encoder(
        self,
        adapter_names: Union[List[str], str],
        text_encoder: Optional["MSPreTrainedModel"] = None,  # noqa: F821
        text_encoder_weights: Optional[Union[float, List[float], List[None]]] = None,
    ):
        """
        Sets the adapter layers for the text encoder.

        Args:
            adapter_names (`List[str]` or `str`):
                The names of the adapters to use.
            text_encoder (`torch.nn.Module`, *optional*):
                The text encoder module to set the adapter layers for. If `None`, it will try to get the `text_encoder`
                attribute.
            text_encoder_weights (`List[float]`, *optional*):
                The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.
        """

        def process_weights(adapter_names, weights):
            # Expand weights into a list, one entry per adapter
            # e.g. for 2 adapters:  7 -> [7,7] ; [3, None] -> [3, None]
            if not isinstance(weights, list):
                weights = [weights] * len(adapter_names)

            if len(adapter_names) != len(weights):
                raise ValueError(
                    f"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(weights)}"
                )

            # Set None values to default of 1.0
            # e.g. [7,7] -> [7,7] ; [3, None] -> [3,1]
            weights = [w if w is not None else 1.0 for w in weights]

            return weights

        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
        text_encoder_weights = process_weights(adapter_names, text_encoder_weights)
        text_encoder = text_encoder or getattr(self, "text_encoder", None)
        if text_encoder is None:
            raise ValueError(
                "The pipeline does not have a default `pipe.text_encoder` class. Please make sure to pass a `text_encoder` instead."
            )
        set_weights_and_activate_adapters(text_encoder, adapter_names, text_encoder_weights)

    def disable_lora_for_text_encoder(self, text_encoder: Optional["MSPreTrainedModel"] = None):
        """
        Disables the LoRA layers for the text encoder.

        Args:
            text_encoder (`torch.nn.Module`, *optional*):
                The text encoder module to disable the LoRA layers for. If `None`, it will try to get the
                `text_encoder` attribute.
        """
        text_encoder = text_encoder or getattr(self, "text_encoder", None)
        if text_encoder is None:
            raise ValueError("Text Encoder not found.")
        set_adapter_layers(text_encoder, enabled=False)

    def enable_lora_for_text_encoder(self, text_encoder: Optional["MSPreTrainedModel"] = None):
        """
        Enables the LoRA layers for the text encoder.

        Args:
            text_encoder (`torch.nn.Module`, *optional*):
                The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`
                attribute.
        """
        text_encoder = text_encoder or getattr(self, "text_encoder", None)
        if text_encoder is None:
            raise ValueError("Text Encoder not found.")
        set_adapter_layers(text_encoder, enabled=True)

    def set_adapters(
        self,
        adapter_names: Union[List[str], str],
        adapter_weights: Optional[Union[float, Dict, List[float], List[Dict]]] = None,
    ):
        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names

        adapter_weights = copy.deepcopy(adapter_weights)

        # Expand weights into a list, one entry per adapter
        if not isinstance(adapter_weights, list):
            adapter_weights = [adapter_weights] * len(adapter_names)

        if len(adapter_names) != len(adapter_weights):
            raise ValueError(
                f"Length of adapter names {len(adapter_names)} is not equal to the length of the weights {len(adapter_weights)}"
            )

        # Decompose weights into weights for unet, text_encoder and text_encoder_2
        unet_lora_weights, text_encoder_lora_weights, text_encoder_2_lora_weights = [], [], []

        list_adapters = self.get_list_adapters()  # eg {"unet": ["adapter1", "adapter2"], "text_encoder": ["adapter2"]}
        all_adapters = {
            adapter for adapters in list_adapters.values() for adapter in adapters
        }  # eg ["adapter1", "adapter2"]
        invert_list_adapters = {
            adapter: [part for part, adapters in list_adapters.items() if adapter in adapters]
            for adapter in all_adapters
        }  # eg {"adapter1": ["unet"], "adapter2": ["unet", "text_encoder"]}

        for adapter_name, weights in zip(adapter_names, adapter_weights):
            if isinstance(weights, dict):
                unet_lora_weight = weights.pop("unet", None)
                text_encoder_lora_weight = weights.pop("text_encoder", None)
                text_encoder_2_lora_weight = weights.pop("text_encoder_2", None)

                if len(weights) > 0:
                    raise ValueError(
                        f"Got invalid key '{weights.keys()}' in lora weight dict for adapter {adapter_name}."
                    )

                if text_encoder_2_lora_weight is not None and not hasattr(self, "text_encoder_2"):
                    logger.warning(
                        "Lora weight dict contains text_encoder_2 weights but will be ignored because pipeline does not have text_encoder_2."
                    )

                # warn if adapter doesn't have parts specified by adapter_weights
                for part_weight, part_name in zip(
                    [unet_lora_weight, text_encoder_lora_weight, text_encoder_2_lora_weight],
                    ["unet", "text_encoder", "text_encoder_2"],
                ):
                    if part_weight is not None and part_name not in invert_list_adapters[adapter_name]:
                        logger.warning(
                            f"Lora weight dict for adapter '{adapter_name}' contains {part_name}, but this will be ignored because {adapter_name} does not contain weights for {part_name}. Valid parts for {adapter_name} are: {invert_list_adapters[adapter_name]}."  # noqa E501
                        )

            else:
                unet_lora_weight = weights
                text_encoder_lora_weight = weights
                text_encoder_2_lora_weight = weights

            unet_lora_weights.append(unet_lora_weight)
            text_encoder_lora_weights.append(text_encoder_lora_weight)
            text_encoder_2_lora_weights.append(text_encoder_2_lora_weight)

        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        # Handle the UNET
        unet.set_adapters(adapter_names, unet_lora_weights)

        # Handle the Text Encoder
        if hasattr(self, "text_encoder"):
            self.set_adapters_for_text_encoder(adapter_names, self.text_encoder, text_encoder_lora_weights)
        if hasattr(self, "text_encoder_2"):
            self.set_adapters_for_text_encoder(adapter_names, self.text_encoder_2, text_encoder_2_lora_weights)

    def disable_lora(self):
        # Disable unet adapters
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        unet.disable_lora()

        # Disable text encoder adapters
        if hasattr(self, "text_encoder"):
            self.disable_lora_for_text_encoder(self.text_encoder)
        if hasattr(self, "text_encoder_2"):
            self.disable_lora_for_text_encoder(self.text_encoder_2)

    def enable_lora(self):
        # Enable unet adapters
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        unet.enable_lora()

        # Enable text encoder adapters
        if hasattr(self, "text_encoder"):
            self.enable_lora_for_text_encoder(self.text_encoder)
        if hasattr(self, "text_encoder_2"):
            self.enable_lora_for_text_encoder(self.text_encoder_2)

    def delete_adapters(self, adapter_names: Union[List[str], str]):
        """
        Args:
        Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).
            adapter_names (`Union[List[str], str]`):
                The names of the adapter to delete. Can be a single string or a list of strings
        """
        if isinstance(adapter_names, str):
            adapter_names = [adapter_names]

        # Delete unet adapters
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        unet.delete_adapters(adapter_names)

        for adapter_name in adapter_names:
            # Delete text encoder adapters
            if hasattr(self, "text_encoder"):
                delete_adapter_layers(self.text_encoder, adapter_name)
            if hasattr(self, "text_encoder_2"):
                delete_adapter_layers(self.text_encoder_2, adapter_name)

    def get_active_adapters(self) -> List[str]:
        """
        Gets the list of the current active adapters.

        Example:

        ```python
        from diffusers import DiffusionPipeline

        pipeline = DiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0",
        ).to("cuda")
        pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
        pipeline.get_active_adapters()
        ```
        """
        from townwish_mindone_testing.diffusers._peft.tuners.tuners_utils import BaseTunerLayer

        active_adapters = []
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        for _, module in unet.cells_and_names():
            if isinstance(module, BaseTunerLayer):
                active_adapters = module.active_adapters
                break

        return active_adapters

    def get_list_adapters(self) -> Dict[str, List[str]]:
        """
        Gets the current list of all available adapters in the pipeline.
        """
        set_adapters = {}

        if hasattr(self, "text_encoder") and hasattr(self.text_encoder, "peft_config"):
            set_adapters["text_encoder"] = list(self.text_encoder.peft_config.keys())

        if hasattr(self, "text_encoder_2") and hasattr(self.text_encoder_2, "peft_config"):
            set_adapters["text_encoder_2"] = list(self.text_encoder_2.peft_config.keys())

        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        if hasattr(self, self.unet_name) and hasattr(unet, "peft_config"):
            set_adapters[self.unet_name] = list(self.unet.peft_config.keys())

        return set_adapters


class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
    """This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""

    # Override to properly handle the loading and unloading of the additional text encoder.
    def load_lora_weights(
        self,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, ms.Tensor]],
        adapter_name: Optional[str] = None,
        **kwargs,
    ):
        """
        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
        `self.text_encoder`.

        All kwargs are forwarded to `self.lora_state_dict`.

        See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.

        See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
        `self.unet`.

        See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
        into `self.text_encoder`.

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
            kwargs (`dict`, *optional*):
                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
        """
        # We could have accessed the unet config from `lora_state_dict()` too. We pass
        # it here explicitly to be able to tell that it's coming from an SDXL
        # pipeline.

        # if a dict is passed, copy it instead of modifying it inplace
        if isinstance(pretrained_model_name_or_path_or_dict, dict):
            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()

        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
        state_dict, network_alphas = self.lora_state_dict(
            pretrained_model_name_or_path_or_dict,
            unet_config=self.unet.config,
            **kwargs,
        )
        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
        if not is_correct_format:
            raise ValueError("Invalid LoRA checkpoint.")

        self.load_lora_into_unet(
            state_dict, network_alphas=network_alphas, unet=self.unet, adapter_name=adapter_name, _pipeline=self
        )
        text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
        if len(text_encoder_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder,
                prefix="text_encoder",
                lora_scale=self.lora_scale,
                adapter_name=adapter_name,
                _pipeline=self,
            )

        text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
        if len(text_encoder_2_state_dict) > 0:
            self.load_lora_into_text_encoder(
                text_encoder_2_state_dict,
                network_alphas=network_alphas,
                text_encoder=self.text_encoder_2,
                prefix="text_encoder_2",
                lora_scale=self.lora_scale,
                adapter_name=adapter_name,
                _pipeline=self,
            )

    @classmethod
    def save_lora_weights(
        cls,
        save_directory: Union[str, os.PathLike],
        unet_lora_layers: Dict[str, Union[nn.Cell, ms.Tensor]] = None,
        text_encoder_lora_layers: Dict[str, Union[nn.Cell, ms.Tensor]] = None,
        text_encoder_2_lora_layers: Dict[str, Union[nn.Cell, ms.Tensor]] = None,
        is_main_process: bool = True,
        weight_name: str = None,
        save_function: Callable = None,
        safe_serialization: bool = True,
    ):
        r"""
        Save the LoRA parameters corresponding to the UNet and text encoder.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to save LoRA parameters to. Will be created if it doesn't exist.
            unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `unet`.
            text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `text_encoder`. Must explicitly pass the text
                encoder LoRA state dict because it comes from 🤗 Transformers.
            text_encoder_2_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
                State dict of the LoRA layers corresponding to the `text_encoder_2`. Must explicitly pass the text
                encoder LoRA state dict because it comes from 🤗 Transformers.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful during distributed training and you
                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful during distributed training when you need to
                replace `torch.save` with another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `True`):
                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
        """
        state_dict = {}

        def pack_weights(layers, prefix):
            layers_weights = {k: v for k, v in layers.parameters_and_names()} if isinstance(layers, nn.Cell) else layers
            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
            return layers_state_dict

        if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
            raise ValueError(
                "You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
            )

        if unet_lora_layers:
            state_dict.update(pack_weights(unet_lora_layers, "unet"))

        if text_encoder_lora_layers:
            state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))

        if text_encoder_2_lora_layers:
            state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))

        cls.write_lora_layers(
            state_dict=state_dict,
            save_directory=save_directory,
            is_main_process=is_main_process,
            weight_name=weight_name,
            save_function=save_function,
            safe_serialization=safe_serialization,
        )

    def _remove_text_encoder_monkey_patch(self):
        recurse_remove_peft_layers(self.text_encoder)
        # TODO: @younesbelkada handle this in transformers side
        if getattr(self.text_encoder, "peft_config", None) is not None:
            del self.text_encoder.peft_config
            self.text_encoder._hf_peft_config_loaded = None

        recurse_remove_peft_layers(self.text_encoder_2)
        if getattr(self.text_encoder_2, "peft_config", None) is not None:
            del self.text_encoder_2.peft_config
            self.text_encoder_2._hf_peft_config_loaded = None


class SD3LoraLoaderMixin:
    r"""
    Load LoRA layers into [`SD3Transformer2DModel`].
    """

    transformer_name = TRANSFORMER_NAME
    num_fused_loras = 0

    def load_lora_weights(
        self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, ms.Tensor]], adapter_name=None, **kwargs
    ):
        """
        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
        `self.text_encoder`.

        All kwargs are forwarded to `self.lora_state_dict`.

        See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.

        See [`~loaders.LoraLoaderMixin.load_lora_into_transformer`] for more details on how the state dict is loaded
        into `self.transformer`.

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
            kwargs (`dict`, *optional*):
                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
        """
        # if a dict is passed, copy it instead of modifying it inplace
        if isinstance(pretrained_model_name_or_path_or_dict, dict):
            pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()

        # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
        state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)

        is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
        if not is_correct_format:
            raise ValueError("Invalid LoRA checkpoint.")

        self.load_lora_into_transformer(
            state_dict,
            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
            adapter_name=adapter_name,
            _pipeline=self,
        )

    @classmethod
    @validate_hf_hub_args
    def lora_state_dict(
        cls,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, ms.Tensor]],
        **kwargs,
    ):
        r"""
        Return state dict for lora weights and the network alphas.

        <Tip warning={true}>

        We support loading A1111 formatted LoRA checkpoints in a limited capacity.

        This function is experimental and might change in the future.

        </Tip>

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                Can be either:

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      with [`ModelMixin.save_pretrained`].
                    - A state dict.

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                is not used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
                incompletely downloaded files are deleted.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only (`bool`, *optional*, defaults to `False`):
                Whether to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            subfolder (`str`, *optional*, defaults to `""`):
                The subfolder location of a model file within a larger model repository on the Hub or locally.

        """
        # Load the main state dict first which has the LoRA layers for either of
        # UNet and text encoder or both.
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)
        weight_name = kwargs.pop("weight_name", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = True
            allow_pickle = True

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }

        model_file = None
        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
            # Let's first try to load .safetensors weights
            if (use_safetensors and weight_name is None) or (
                weight_name is not None and weight_name.endswith(".safetensors")
            ):
                try:
                    model_file = _get_model_file(
                        pretrained_model_name_or_path_or_dict,
                        weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=resume_download,
                        proxies=proxies,
                        local_files_only=local_files_only,
                        token=token,
                        revision=revision,
                        subfolder=subfolder,
                        user_agent=user_agent,
                    )
                    state_dict = load_file(model_file)
                except (IOError, safetensors.SafetensorError) as e:
                    if not allow_pickle:
                        raise e
                    # try loading non-safetensors weights
                    model_file = None
                    pass

            if model_file is None:
                model_file = _get_model_file(
                    pretrained_model_name_or_path_or_dict,
                    weights_name=weight_name or LORA_WEIGHT_NAME,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    token=token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                )
                raise NotImplementedError(
                    f"Only supports deserialization of weights file in safetensors format, but got {model_file}"
                )
        else:
            state_dict = pretrained_model_name_or_path_or_dict

        return state_dict

    @classmethod
    def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None, _pipeline=None):
        """
        This will load the LoRA layers specified in `state_dict` into `transformer`.

        Parameters:
            state_dict (`dict`):
                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
                encoder lora layers.
            transformer (`SD3Transformer2DModel`):
                The Transformer model to load the LoRA layers into.
            adapter_name (`str`, *optional*):
                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
                `default_{i}` where i is the total number of adapters being loaded.
        """
        from townwish_mindone_testing.diffusers._peft import (
            LoraConfig,
            inject_adapter_in_model,
            set_peft_model_state_dict,
        )

        keys = list(state_dict.keys())

        transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
        state_dict = {
            k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
        }

        if len(state_dict.keys()) > 0:
            if adapter_name in getattr(transformer, "peft_config", {}):
                raise ValueError(
                    f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
                )

            rank = {}
            for key, val in state_dict.items():
                if "lora_B" in key:
                    rank[key] = val.shape[1]

            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=None, peft_state_dict=state_dict)
            if "use_dora" in lora_config_kwargs:
                if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                    raise ValueError(
                        "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
                    )
                else:
                    lora_config_kwargs.pop("use_dora")
            lora_config = LoraConfig(**lora_config_kwargs)

            # adapter_name
            if adapter_name is None:
                adapter_name = get_adapter_name(transformer)

            inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
            incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)

            if incompatible_keys is not None:
                # check only for unexpected keys
                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
                if unexpected_keys:
                    logger.warning(
                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
                        f" {unexpected_keys}. "
                    )

    @classmethod
    def save_lora_weights(
        cls,
        save_directory: Union[str, os.PathLike],
        transformer_lora_layers: Dict[str, nn.Cell] = None,
        is_main_process: bool = True,
        weight_name: str = None,
        save_function: Callable = None,
        safe_serialization: bool = True,
    ):
        r"""
        Save the LoRA parameters corresponding to the UNet and text encoder.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to save LoRA parameters to. Will be created if it doesn't exist.
            transformer_lora_layers (`Dict[str, ms.nn.Cell]` or `Dict[str, ms.Tensor]`):
                State dict of the LoRA layers corresponding to the `transformer`.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful during distributed training and you
                need to call this function on all processes. In this case, set `is_main_process=True` only on the main
                process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful during distributed training when you need to
                replace `torch.save` with another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `True`):
                Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
        """
        state_dict = {}

        def pack_weights(layers, prefix):
            layers_weights = {k: v for k, v in layers.parameters_and_names()} if isinstance(layers, nn.Cell) else layers
            layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
            return layers_state_dict

        if not transformer_lora_layers:
            raise ValueError("You must pass `transformer_lora_layers`.")

        if transformer_lora_layers:
            state_dict.update(pack_weights(transformer_lora_layers, cls.transformer_name))

        # Save the model
        cls.write_lora_layers(
            state_dict=state_dict,
            save_directory=save_directory,
            is_main_process=is_main_process,
            weight_name=weight_name,
            save_function=save_function,
            safe_serialization=safe_serialization,
        )

    @staticmethod
    def write_lora_layers(
        state_dict: Dict[str, ms.Tensor],
        save_directory: str,
        is_main_process: bool,
        weight_name: str,
        save_function: Callable,
        safe_serialization: bool,
    ):
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        if save_function is None:
            if safe_serialization:

                def save_function(weights, filename):
                    return save_file(weights, filename, metadata={"format": "np"})

            else:
                save_function = ms.save_checkpoint

        os.makedirs(save_directory, exist_ok=True)

        if weight_name is None:
            if safe_serialization:
                weight_name = LORA_WEIGHT_NAME_SAFE
            else:
                weight_name = LORA_WEIGHT_NAME

        save_path = Path(save_directory, weight_name).as_posix()
        save_function(state_dict, save_path)
        logger.info(f"Model weights saved in {save_path}")

    def unload_lora_weights(self):
        """
        Unloads the LoRA parameters.

        Examples:

        ```python
        >>> # Assuming `pipeline` is already loaded with the LoRA parameters.
        >>> pipeline.unload_lora_weights()
        >>> ...
        ```
        """
        transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
        recurse_remove_peft_layers(transformer)
        if hasattr(transformer, "peft_config"):
            del transformer.peft_config
