


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Tuple:
				# load base model
				lowerCAmelCase_       :					Dict			 = StableDiffusionPipeline.from_pretrained(_a       ,      torch_dtype=torch.floataa       )

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

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

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

								lowerCAmelCase_       :					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:
												lowerCAmelCase_       :					Dict			 = state_dict[pair_keys[0]].squeeze(3       ).squeeze(2       ).to(torch.floataa       )
												lowerCAmelCase_       :					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:
												lowerCAmelCase_       :					Optional[int]			 = state_dict[pair_keys[0]].to(torch.floataa       )
												lowerCAmelCase_       :					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__":
						_UpperCAmelCase	:     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.)""")

						_UpperCAmelCase	:     str   =parser.parse_args()

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

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

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




"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





