





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Any   ,    _lowercase     : List[Any]   ,    _lowercase     : str   ,    _lowercase     : Optional[Any]			)   ->							Tuple:




		'''simple docstring'''
		# load base model
		lowercase__					:      Optional[int]      =		StableDiffusionPipeline.from_pretrained(_lowercase   ,    torch_dtype=torch.floataa			)

		# load LoRA weight from .safetensors
		lowercase__					:      Dict      =		load_file(_lowercase			)

		lowercase__					:      List[str]      =		[]

		# 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:
						lowercase__					:      Optional[Any]      =		key.split('.'			)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'			)[-1].split('_'			)
						lowercase__					:      Any      =		pipeline.text_encoder
				else:
						lowercase__					:      Union[str, Any]      =		key.split('.'			)[0].split(LORA_PREFIX_UNET + '_'			)[-1].split('_'			)
						lowercase__					:      Tuple      =		pipeline.unet

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

				lowercase__					:      Optional[int]      =		[]
				if "lora_down" in key:
						pair_keys.append(key.replace('lora_down'   ,    'lora_up'			)			)
						pair_keys.append(_lowercase			)
				else:
						pair_keys.append(_lowercase			)
						pair_keys.append(key.replace('lora_up'   ,    'lora_down'			)			)

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

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

		return pipeline


if __name__ == "__main__":
			__UpperCamelCase:	Optional[Any] = 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=0.7_5, 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.)""")

			__UpperCamelCase:	Union[str, Any] = parser.parse_args()

			__UpperCamelCase:	List[Any] = args.base_model_path
			__UpperCamelCase:	List[Any] = args.checkpoint_path
			__UpperCamelCase:	List[Any] = args.dump_path
			__UpperCamelCase:	Optional[int] = args.lora_prefix_unet
			__UpperCamelCase:	Optional[Any] = args.lora_prefix_text_encoder
			__UpperCamelCase:	int = args.alpha

			__UpperCamelCase:	Tuple = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			__UpperCamelCase:	Dict = pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


