



'''simple docstring'''






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
		# load base model
		a		:      List[Any]       =  StableDiffusionPipeline.from_pretrained(_A				,    torch_dtype=torch.floataa       )

		# load LoRA weight from .safetensors
		a		:      Optional[int]       =  load_file(_A       )

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

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

				a		:      str       =  []
				if "lora_down" in key:
						pair_keys.append(key.replace('lora_down'				,    'lora_up'       )       )
						pair_keys.append(_A       )
				else:
						pair_keys.append(_A       )
						pair_keys.append(key.replace('lora_up'				,    'lora_down'       )       )

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

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

		return pipeline


if __name__ == "__main__":
					lowerCAmelCase:      Tuple		 =		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.)')

					lowerCAmelCase:      List[str]		 =		parser.parse_args()

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

					lowerCAmelCase:      Optional[Any]		 =		convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

					lowerCAmelCase:      Union[str, Any]		 =		pipe.to(args.device)
					pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




'''simple docstring'''






from ..utils import DummyObject, requires_backends

class 							a__(      metaclass=lowerCamelCase__			):
    lowercase__				=					["""torch""", """torchsde"""]







    def __init__(   self				:						Any		,    *__snake_case				:						List[str]		,    **__snake_case				:						Tuple			):
      requires_backends(self		,    ['torch', 'torchsde']			)







    @classmethod
    def     lowercase_ (   cls				:						List[Any]		,    *__snake_case				:						str		,    **__snake_case				:						List[Any]			):
      requires_backends(cls		,    ['torch', 'torchsde']			)







    @classmethod
    def     lowercase_ (   cls				:						Dict		,    *__snake_case				:						Optional[Any]		,    **__snake_case				:						str			):
      requires_backends(cls		,    ['torch', 'torchsde']			)