



"""simple docstring"""






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					List[str]			):
			"""simple docstring"""
			# load base model
			lowerCamelCase__	:	Tuple				=StableDiffusionPipeline.from_pretrained(__lowerCamelCase							,	torch_dtype=torch.floataa			)

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

			lowerCamelCase__	:	Optional[int]				=[]

			# 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__	:	List[str]				=key.split('''.'''			)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''			)[-1].split('''_'''			)
									lowerCamelCase__	:	Optional[Any]				=pipeline.text_encoder
						else:
									lowerCamelCase__	:	Dict				=key.split('''.'''			)[0].split(LORA_PREFIX_UNET + '''_'''			)[-1].split('''_'''			)
									lowerCamelCase__	:	Any				=pipeline.unet

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

						lowerCamelCase__	:	Tuple				=[]
						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__	:	List[Any]				=state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
									lowerCamelCase__	:	Dict				=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__	:	str				=state_dict[pair_keys[0]].to(torch.floataa			)
									lowerCamelCase__	:	Union[str, Any]				=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				:				int   		=		parser.parse_args()

		_lowercase				:				Tuple   		=		args.base_model_path
		_lowercase				:				Dict   		=		args.checkpoint_path
		_lowercase				:				Any   		=		args.dump_path
		_lowercase				:				str   		=		args.lora_prefix_unet
		_lowercase				:				Union[str, Any]   		=		args.lora_prefix_text_encoder
		_lowercase				:				Dict   		=		args.alpha

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

		_lowercase				:				int   		=		pipe.to(args.device)
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase				:				Union[str, Any]   		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




