



'''simple docstring'''
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						# load base model
						_UpperCamelCase   :						str												=  StableDiffusionPipeline.from_pretrained(UpperCAmelCase_    ,    torch_dtype=torch.floataa				)

						# load LoRA weight from .safetensors
						_UpperCamelCase   :						Optional[Any]												=  load_file(UpperCAmelCase_				)

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

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

												_UpperCamelCase   :						Union[str, Any]												=  []
												if "lora_down" in key:
																		pair_keys.append(key.replace('lora_down'    ,    'lora_up'				)				)
																		pair_keys.append(UpperCAmelCase_				)
												else:
																		pair_keys.append(UpperCAmelCase_				)
																		pair_keys.append(key.replace('lora_up'    ,    'lora_down'				)				)

												# update weight
												if len(state_dict[pair_keys[0]].shape				) == 4:
																		_UpperCamelCase   :						int												=  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(UpperCAmelCase_    ,    UpperCAmelCase_				).unsqueeze(2				).unsqueeze(3				)
												else:
																		_UpperCamelCase   :						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(UpperCAmelCase_    ,    UpperCAmelCase_				)

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

						return pipeline


if __name__ == "__main__":
					snake_case_					:      int    			=    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_					:      List[str]    			=    args.base_model_path
					snake_case_					:      Union[str, Any]    			=    args.checkpoint_path
					snake_case_					:      List[Any]    			=    args.dump_path
					snake_case_					:      Tuple    			=    args.lora_prefix_unet
					snake_case_					:      Dict    			=    args.lora_prefix_text_encoder
					snake_case_					:      str    			=    args.alpha

					snake_case_					:      Dict    			=    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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







'''simple docstring'''
def A__       (	UpperCAmelCase_ = 1_0_0_0				):
						_UpperCamelCase   :						List[str]												=  3
						_UpperCamelCase   :						Any												=  0
						while a < n:
												if a % 3 == 0 or a % 5 == 0:
																		result += a
												elif a % 1_5 == 0:
																		result -= a
												a += 1
						return result


if __name__ == "__main__":
					print(F"""{solution() = }""")


