





"""simple docstring"""




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					int					, _lowercase		:					List[Any]					, _lowercase		:					List[Any]					, _lowercase		:					Union[str, Any]      ):






	'''simple docstring'''






	a   :	List[str]			=       StableDiffusionPipeline.from_pretrained(__UpperCAmelCase					, torch_dtype=torch.floataa      )

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

	a   :	Any			=       []

	# 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   :	str			=       key.split("."      )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"      )[-1].split("_"      )
			a   :	List[str]			=       pipeline.text_encoder
		else:
			a   :	Optional[int]			=       key.split("."      )[0].split(LORA_PREFIX_UNET + "_"      )[-1].split("_"      )
			a   :	Any			=       pipeline.unet

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

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

		a		:				Any  			=    parser.parse_args()

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

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

		a		:				Dict  			=    pipe.to(args.device)
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


