



'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# load base model
		_a	: Union[str, Any]								=				StableDiffusionPipeline.from_pretrained(lowerCAmelCase_		,     torch_dtype=torch.floataa    )

		# load LoRA weight from .safetensors
		_a	: Optional[int]								=				load_file(lowerCAmelCase_    )

		_a	: 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:
						_a	: List[Any]								=				key.split('.'    )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'    )[-1].split('_'    )
						_a	: int								=				pipeline.text_encoder
				else:
						_a	: Tuple								=				key.split('.'    )[0].split(LORA_PREFIX_UNET + '_'    )[-1].split('_'    )
						_a	: List[Any]								=				pipeline.unet

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

				_a	: List[Any]								=				[]
				if "lora_down" in key:
						pair_keys.append(key.replace('lora_down'		,     'lora_up'    )    )
						pair_keys.append(lowerCAmelCase_    )
				else:
						pair_keys.append(lowerCAmelCase_    )
						pair_keys.append(key.replace('lora_up'		,     'lora_down'    )    )

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

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

		return pipeline


if __name__ == "__main__":
							__lowerCAmelCase		     =  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		     =  parser.parse_args()

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

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

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









'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							__lowerCAmelCase		     =  True
except (ImportError, ModuleNotFoundError):
							__lowerCAmelCase		     =  False

if NLTK_AVAILABLE:
							with FileLock('''.lock''') as lock:
														nltk.download('''punkt''', quiet=True)



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		re.sub('<n>'		,     ''		,     lowerCAmelCase_    )  # remove pegasus newline char
		assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
		return "\n".join(nltk.sent_tokenize(lowerCAmelCase_    )    )




