


'''simple docstring'''






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Dict:
							# load base model
							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__":
		__UpperCAmelCase       							=				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.)''')

		__UpperCAmelCase       							=				parser.parse_args()

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

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

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



'''simple docstring'''






def    _snake_case  (	A			)		->       int:
							if n == 1 or not isinstance(A					,       A			):
														return 0
							elif n == 2:
														return 1
							else:
														lowerCAmelCase__			  =      [0, 1]
														for i in range(2					,       n + 1			):
																					sequence.append(sequence[i - 1] + sequence[i - 2]			)

														return sequence[n]


def    _snake_case  (	A			)		->       int:
							lowerCAmelCase__			  =      0
							lowerCAmelCase__			  =      2

							while digits < n:
														index += 1
														lowerCAmelCase__			  =      len(str(fibonacci(A			)			)			)

							return index


def    _snake_case  (	A = 1000			)		->       int:
							return fibonacci_digits_index(A			)


if __name__ == "__main__":
		print(solution(int(str(input()).strip())))