



'''simple docstring'''




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					StableDiffusionPipeline.from_pretrained(lowerCamelCase__						,				torch_dtype=torch.floataa   )

			# load LoRA weight from .safetensors
			A_     :						Tuple     =					load_file(lowerCamelCase__   )

			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_     :						Tuple     =					key.split("""."""   )[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""   )[-1].split("""_"""   )
									A_     :						List[str]     =					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_     :						List[str]     =					layer_infos.pop(0   )
						while len(lowerCamelCase__   ) > -1:
									try:
												A_     :						List[Any]     =					curr_layer.__getattr__(lowerCamelCase__   )
												if len(lowerCamelCase__   ) > 0:
															A_     :						Optional[Any]     =					layer_infos.pop(0   )
												elif len(lowerCamelCase__   ) == 0:
															break
									except Exception:
												if len(lowerCamelCase__   ) > 0:
															temp_name += "_" + layer_infos.pop(0   )
												else:
															A_     :						int     =					layer_infos.pop(0   )

						A_     :						Optional[int]     =					[]
						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_     :						Dict     =					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(lowerCamelCase__						,				lowerCamelCase__   ).unsqueeze(2   ).unsqueeze(3   )
						else:
									A_     :						Optional[int]     =					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							: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.)''')

						lowerCamelCase							:Any       = parser.parse_args()

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

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

						lowerCamelCase							:List[str]       = pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)