import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Any				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Tuple			)							->      Any:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline.from_pretrained(lowercase_				,  torch_dtype=torch.floataa			)

		# load LoRA weight from .safetensors
		SCREAMING_SNAKE_CASE__    							=   load_file(lowercase_			)

		SCREAMING_SNAKE_CASE__    							=   []

		# 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:
						SCREAMING_SNAKE_CASE__    							=   key.split("""."""			)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""			)[-1].split("""_"""			)
						SCREAMING_SNAKE_CASE__    							=   pipeline.text_encoder
				else:
						SCREAMING_SNAKE_CASE__    							=   key.split("""."""			)[0].split(LORA_PREFIX_UNET + """_"""			)[-1].split("""_"""			)
						SCREAMING_SNAKE_CASE__    							=   pipeline.unet

				# find the target layer
				SCREAMING_SNAKE_CASE__    							=   layer_infos.pop(0			)
				while len(lowercase_			) > -1:
						try:
								SCREAMING_SNAKE_CASE__    							=   curr_layer.__getattr__(lowercase_			)
								if len(lowercase_			) > 0:
										SCREAMING_SNAKE_CASE__    							=   layer_infos.pop(0			)
								elif len(lowercase_			) == 0:
										break
						except Exception:
								if len(lowercase_			) > 0:
										temp_name += "_" + layer_infos.pop(0			)
								else:
										SCREAMING_SNAKE_CASE__    							=   layer_infos.pop(0			)

				SCREAMING_SNAKE_CASE__    							=   []
				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:
						SCREAMING_SNAKE_CASE__    							=   state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
						SCREAMING_SNAKE_CASE__    							=   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:
						SCREAMING_SNAKE_CASE__    							=   state_dict[pair_keys[0]].to(torch.floataa			)
						SCREAMING_SNAKE_CASE__    							=   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__":
			__lowerCamelCase    :				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.)''')

			__lowerCamelCase    :				str							     =	parser.parse_args()

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

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

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

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  if index == number_of_items:
    return 0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   knapsack(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  index + 1			)
  if weights[index] <= max_weight:
    SCREAMING_SNAKE_CASE__    							=   values[index] + knapsack(
        __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  max_weight - weights[index]				,  index + 1			)
  return max(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
