





'''simple docstring'''







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





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

						# load LoRA weight from .safetensors
						UpperCAmelCase_	:					Any     =     load_file(SCREAMING_SNAKE_CASE__      )

						UpperCAmelCase_	:					Dict     =     []

						# 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:
																		UpperCAmelCase_	:					Optional[Any]     =     key.split('''.'''      )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''      )[-1].split('''_'''      )
																		UpperCAmelCase_	:					Optional[int]     =     pipeline.text_encoder
												else:
																		UpperCAmelCase_	:					Optional[int]     =     key.split('''.'''      )[0].split(LORA_PREFIX_UNET + '''_'''      )[-1].split('''_'''      )
																		UpperCAmelCase_	:					int     =     pipeline.unet

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

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

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

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

						return pipeline


if __name__ == "__main__":
					snake_case_   :				List[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=0.75, 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.)")

					snake_case_   :				Tuple		=     parser.parse_args()

					snake_case_   :				int		=     args.base_model_path
					snake_case_   :				Tuple		=     args.checkpoint_path
					snake_case_   :				Optional[int]		=     args.dump_path
					snake_case_   :				Dict		=     args.lora_prefix_unet
					snake_case_   :				Dict		=     args.lora_prefix_text_encoder
					snake_case_   :				Optional[Any]		=     args.alpha

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

					snake_case_   :				Any		=     pipe.to(args.device)
					pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











import math
import sys



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  if number != int(lowerCamelCase      ):
    raise ValueError("the value of input must be a natural number"      )
  if number < 0:
    raise ValueError("the value of input must not be a negative number"      )
  if number == 0:
    return 1
  lowercase			:Dict							=  [-1] * (number + 1)
  lowercase			:Optional[Any]							=  0
  for i in range(1,	number + 1      ):
    lowercase			:Optional[int]							=  sys.maxsize
    lowercase			:Any							=  int(math.sqrt(lowerCamelCase      )      )
    for j in range(1,	root + 1      ):
      lowercase			:Any							=  1 + answers[i - (j**2)]
      lowercase			:Dict							=  min(lowerCamelCase,	lowerCamelCase      )
    lowercase			:Union[str, Any]							=  answer
  return answers[number]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



