

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 		UpperCamelCase      (lowercase_:				Optional[int]					,   lowercase_:				str					,   lowercase_:				Any					,   lowercase_:				List[Any]					,   lowercase_:				Tuple      )  ->   Any:
	# load base model
	A__							:					List[str]       =	StableDiffusionPipeline.from_pretrained(lowercase_					,   torch_dtype=torch.floataa      )

	# load LoRA weight from .safetensors
	A__							:					Dict       =	load_file(lowercase_      )

	A__							:					List[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:
			A__							:					Optional[Any]       =	key.split("""."""      )[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""      )[-1].split("""_"""      )
			A__							:					str       =	pipeline.text_encoder
		else:
			A__							:					Any       =	key.split("""."""      )[0].split(LORA_PREFIX_UNET + """_"""      )[-1].split("""_"""      )
			A__							:					Union[str, Any]       =	pipeline.unet

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

		A__							:					str       =	[]
		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:
			A__							:					List[str]       =	state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
			A__							:					Optional[int]       =	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:
			A__							:					str       =	state_dict[pair_keys[0]].to(torch.floataa      )
			A__							:					List[Any]       =	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__":
	A_ :				Dict						 =    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.)')

	A_ :				str						 =    parser.parse_args()

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

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

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






def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				str      )  ->   bool:
	A__							:					Union[str, Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	[[False for _ in range(m + 1      )] for _ in range(n + 1      )]
	A__							:					str       =	True
	for i in range(lowercase_      ):
		for j in range(m + 1      ):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					A__							:					int       =	True
				if a[i].islower():
					A__							:					Dict       =	True
	return dp[n][m]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



