


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =StableDiffusionPipeline.from_pretrained(__UpperCAmelCase	,torch_dtype=torch.floataa)

				# load LoRA weight from .safetensors
				_SCREAMING_SNAKE_CASE			  =load_file(__UpperCAmelCase)

				_SCREAMING_SNAKE_CASE			  =[]

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

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

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

							snake_case_						:			Tuple													=							parser.parse_args()

							snake_case_						:			Tuple													=							args.base_model_path
							snake_case_						:			Any													=							args.checkpoint_path
							snake_case_						:			Optional[Any]													=							args.dump_path
							snake_case_						:			Optional[int]													=							args.lora_prefix_unet
							snake_case_						:			Tuple													=							args.lora_prefix_text_encoder
							snake_case_						:			str													=							args.alpha

							snake_case_						:			Any													=							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

							snake_case_						:			Tuple													=							pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_						:			List[Any]													=							{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			str													=							[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							snake_case_						:			Union[str, Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)