




"""simple docstring"""

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def      UpperCamelCase__   (						lowercase__     :   Union[str, Any]				,   lowercase__     :   Tuple				,   lowercase__     :   Optional[Any]				,   lowercase__     :   Any				,   lowercase__     :   Dict      ):
		# load base model
		snake_case						:  str    	= StableDiffusionPipeline.from_pretrained(lowercase__				,   torch_dtype=torch.floataa      )

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

		snake_case						:  List[str]    	= []

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

				# find the target layer
				snake_case						:  Tuple    	= layer_infos.pop(0      )
				while len(lowercase__      ) > -1:
						try:
								snake_case						:  List[str]    	= curr_layer.__getattr__(lowercase__      )
								if len(lowercase__      ) > 0:
										snake_case						:  str    	= layer_infos.pop(0      )
								elif len(lowercase__      ) == 0:
										break
						except Exception:
								if len(lowercase__      ) > 0:
										temp_name += "_" + layer_infos.pop(0      )
								else:
										snake_case						:  Tuple    	= 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(lowercase__      )
				else:
						pair_keys.append(lowercase__      )
						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						:  str    	= state_dict[pair_keys[1]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
						curr_layer.weight.data += alpha * torch.mm(lowercase__				,   lowercase__      ).unsqueeze(2      ).unsqueeze(3      )
				else:
						snake_case						:  Tuple    	= state_dict[pair_keys[0]].to(torch.floataa      )
						snake_case						:  List[Any]    	= state_dict[pair_keys[1]].to(torch.floataa      )
						curr_layer.weight.data += alpha * torch.mm(lowercase__				,   lowercase__      )

				# update visited list
				for item in pair_keys:
						visited.append(lowercase__      )

		return pipeline


if __name__ == "__main__":
							__A       						=	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       						=	parser.parse_args()

							__A       						=	args.base_model_path
							__A       						=	args.checkpoint_path
							__A       						=	args.dump_path
							__A       						=	args.lora_prefix_unet
							__A       						=	args.lora_prefix_text_encoder
							__A       						=	args.alpha

							__A       						=	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

							__A       						=	pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Dict    ,      *UpperCAmelCase__       :      Dict    ,      **UpperCAmelCase__       :      List[str]    )				->  None:
        warnings.warn(
            'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use FlavaImageProcessor instead.'    ,      UpperCAmelCase__    ,      )
        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





