



"""simple docstring"""

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def __lowercase			(							_a		,			_a		,			_a		,			_a		,			_a    ):
    # load base model
    snake_case_ :     Dict				=	StableDiffusionPipeline.from_pretrained(_a		,			torch_dtype=torch.floataa    )

    # load LoRA weight from .safetensors
    snake_case_ :     List[Any]				=	load_file(_a    )

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

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

        snake_case_ :     Optional[int]				=	[]
        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:
            snake_case_ :     Dict				=	state_dict[pair_keys[0]].squeeze(3    ).squeeze(2    ).to(torch.floataa    )
            snake_case_ :     List[Any]				=	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:
            snake_case_ :     Optional[int]				=	state_dict[pair_keys[0]].to(torch.floataa    )
            snake_case_ :     Tuple				=	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__    :  Optional[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.)''')

     lowercase__    :  str							    =	parser.parse_args()

     lowercase__    :  int							    =	args.base_model_path
     lowercase__    :  Union[str, Any]							    =	args.checkpoint_path
     lowercase__    :  List[Any]							    =	args.dump_path
     lowercase__    :  Optional[int]							    =	args.lora_prefix_unet
     lowercase__    :  Dict							    =	args.lora_prefix_text_encoder
     lowercase__    :  List[str]							    =	args.alpha

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

     lowercase__    :  Optional[int]							    =	pipe.to(args.device)
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










"""simple docstring"""

from functools import lru_cache




@lru_cache
def __lowercase			(							_a    ):
    if num < 0:
        raise ValueError('''Number should not be negative.'''    )

    return 1 if num in (0, 1) else num * factorial(num - 1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





