



"""simple docstring"""






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Dict							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	int				=StableDiffusionPipeline.from_pretrained(__lowerCamelCase							,	torch_dtype=torch.floataa			)

			# load LoRA weight from .safetensors
			lowerCamelCase__	:	str				=load_file(__lowerCamelCase			)

			lowerCamelCase__	:	Any				=[]

			# directly update weight in diffusers model
			for key in state_dict:
						# it is suggested to print out the key, it usually will be something like below
						# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

						# as we have set the alpha beforehand, so just skip
						if ".alpha" in key or key in visited:
									continue

						if "text" in key:
									lowerCamelCase__	:	int				=key.split('''.'''			)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''			)[-1].split('''_'''			)
									lowerCamelCase__	:	Optional[Any]				=pipeline.text_encoder
						else:
									lowerCamelCase__	:	Tuple				=key.split('''.'''			)[0].split(LORA_PREFIX_UNET + '''_'''			)[-1].split('''_'''			)
									lowerCamelCase__	:	List[str]				=pipeline.unet

						# find the target layer
						lowerCamelCase__	:	Dict				=layer_infos.pop(0			)
						while len(__lowerCamelCase			) > -1:
									try:
												lowerCamelCase__	:	List[str]				=curr_layer.__getattr__(__lowerCamelCase			)
												if len(__lowerCamelCase			) > 0:
															lowerCamelCase__	:	List[Any]				=layer_infos.pop(0			)
												elif len(__lowerCamelCase			) == 0:
															break
									except Exception:
												if len(__lowerCamelCase			) > 0:
															temp_name += "_" + layer_infos.pop(0			)
												else:
															lowerCamelCase__	:	Optional[int]				=layer_infos.pop(0			)

						lowerCamelCase__	:	List[Any]				=[]
						if "lora_down" in key:
									pair_keys.append(key.replace('''lora_down'''							,	'''lora_up'''			)			)
									pair_keys.append(__lowerCamelCase			)
						else:
									pair_keys.append(__lowerCamelCase			)
									pair_keys.append(key.replace('''lora_up'''							,	'''lora_down'''			)			)

						# update weight
						if len(state_dict[pair_keys[0]].shape			) == 4:
									lowerCamelCase__	:	int				=state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
									lowerCamelCase__	:	Union[str, Any]				=state_dict[pair_keys[1]].squeeze(3			).squeeze(2			).to(torch.floataa			)
									curr_layer.weight.data += alpha * torch.mm(__lowerCamelCase							,	__lowerCamelCase			).unsqueeze(2			).unsqueeze(3			)
						else:
									lowerCamelCase__	:	Union[str, Any]				=state_dict[pair_keys[0]].to(torch.floataa			)
									lowerCamelCase__	:	Dict				=state_dict[pair_keys[1]].to(torch.floataa			)
									curr_layer.weight.data += alpha * torch.mm(__lowerCamelCase							,	__lowerCamelCase			)

						# update visited list
						for item in pair_keys:
									visited.append(__lowerCamelCase			)

			return pipeline


if __name__ == "__main__":
		_lowercase				:				int   		=argparse.ArgumentParser()

		parser.add_argument(
		    "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
		)
		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
		)
		parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
		parser.add_argument(
		    "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
		)
		parser.add_argument(
		    "--lora_prefix_text_encoder",
		    default="lora_te",
		    type=str,
		    help="The prefix of text encoder weight in safetensors",
		)
		parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
		parser.add_argument(
		    "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
		)
		parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

		_lowercase				:				Optional[Any]   		=parser.parse_args()

		_lowercase				:				Optional[Any]   		=args.base_model_path
		_lowercase				:				int   		=args.checkpoint_path
		_lowercase				:				Tuple   		=args.dump_path
		_lowercase				:				int   		=args.lora_prefix_unet
		_lowercase				:				Dict   		=args.lora_prefix_text_encoder
		_lowercase				:				List[str]   		=args.alpha

		_lowercase				:				int   		=convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

		_lowercase				:				List[Any]   		=pipe.to(args.device)
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









"""simple docstring"""






_lowercase				:				Optional[Any]   		=		{
    "Pillow": "Pillow<10.0.0",
    "accelerate": "accelerate>=0.20.3",
    "av": "av==9.2.0",
    "beautifulsoup4": "beautifulsoup4",
    "black": "black~=23.1",
    "codecarbon": "codecarbon==1.2.0",
    "cookiecutter": "cookiecutter==1.7.3",
    "dataclasses": "dataclasses",
    "datasets": "datasets!=2.5.0",
    "decord": "decord==0.6.0",
    "deepspeed": "deepspeed>=0.9.3",
    "diffusers": "diffusers",
    "dill": "dill<0.3.5",
    "evaluate": "evaluate>=0.2.0",
    "fairscale": "fairscale>0.3",
    "faiss-cpu": "faiss-cpu",
    "fastapi": "fastapi",
    "filelock": "filelock",
    "flax": "flax>=0.4.1,<=0.7.0",
    "ftfy": "ftfy",
    "fugashi": "fugashi>=1.0",
    "GitPython": "GitPython<3.1.19",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
    "importlib_metadata": "importlib_metadata",
    "ipadic": "ipadic>=1.0.0,<2.0",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
    "jaxlib": "jaxlib>=0.1.65,<=0.4.13",
    "jieba": "jieba",
    "kenlm": "kenlm",
    "keras-nlp": "keras-nlp>=0.3.1",
    "librosa": "librosa",
    "nltk": "nltk",
    "natten": "natten>=0.14.6",
    "numpy": "numpy>=1.17",
    "onnxconverter-common": "onnxconverter-common",
    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
    "onnxruntime": "onnxruntime>=1.4.0",
    "opencv-python": "opencv-python",
    "optuna": "optuna",
    "optax": "optax>=0.0.8,<=0.1.4",
    "packaging": "packaging>=20.0",
    "parameterized": "parameterized",
    "phonemizer": "phonemizer",
    "protobuf": "protobuf",
    "psutil": "psutil",
    "pyyaml": "pyyaml>=5.1",
    "pydantic": "pydantic<2",
    "pytest": "pytest>=7.2.0",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "python": "python>=3.8.0",
    "ray[tune]": "ray[tune]",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "rhoknp": "rhoknp>=1.1.0,<1.3.1",
    "rjieba": "rjieba",
    "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
    "ruff": "ruff>=0.0.241,<=0.0.259",
    "sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
    "sacremoses": "sacremoses",
    "safetensors": "safetensors>=0.3.1",
    "sagemaker": "sagemaker>=2.31.0",
    "scikit-learn": "scikit-learn",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "sigopt": "sigopt",
    "starlette": "starlette",
    "sudachipy": "sudachipy>=0.6.6",
    "sudachidict_core": "sudachidict_core>=20220729",
    "tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
    "tensorflow": "tensorflow>=2.6,<2.14",
    "tensorflow-text": "tensorflow-text<2.14",
    "tf2onnx": "tf2onnx",
    "timeout-decorator": "timeout-decorator",
    "timm": "timm",
    "tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
    "torch": "torch>=1.9,!=1.12.0",
    "torchaudio": "torchaudio",
    "torchvision": "torchvision",
    "pyctcdecode": "pyctcdecode>=0.4.0",
    "tqdm": "tqdm>=4.27",
    "unidic": "unidic>=1.0.2",
    "unidic_lite": "unidic_lite>=1.0.7",
    "urllib3": "urllib3<2.0.0",
    "uvicorn": "uvicorn",
}




