'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=StableDiffusionPipeline.from_pretrained(a__			,      torch_dtype=torch.floataa      )

						# load LoRA weight from .safetensors
						_lowerCAmelCase										=load_file(a__      )

						_lowerCAmelCase										=[]

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

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

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

	lowercase_     		= parser.parse_args()

	lowercase_     		= args.base_model_path
	lowercase_     		= args.checkpoint_path
	lowercase_     		= args.dump_path
	lowercase_     		= args.lora_prefix_unet
	lowercase_     		= args.lora_prefix_text_encoder
	lowercase_     		= args.alpha

	lowercase_     		= convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	lowercase_     		= pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






'''simple docstring'''

from __future__ import annotations

lowercase_     		= 10



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=1
						_lowerCAmelCase										=max(a__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												_lowerCAmelCase										=[[] for _ in range(a__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		_lowerCAmelCase										=int((i / placement) % RADIX      )
																		buckets[tmp].append(a__      )
												# put each buckets' contents into list_of_ints
												_lowerCAmelCase										=0
												for b in range(a__      ):
																		for i in buckets[b]:
																								_lowerCAmelCase										=i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
							import doctest

							doctest.testmod()





