



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       int						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[int]						,       _lowercase    :       Tuple)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]													=  StableDiffusionPipeline.from_pretrained(_lowercase						,       torch_dtype=torch.floataa)

			# load LoRA weight from .safetensors
			a__       :						Union[str, Any]													=  load_file(_lowercase)

			a__       :						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:
									a__       :						Dict													=  key.split(""".""")[0].split(LORA_PREFIX_TEXT_ENCODER + """_""")[-1].split("""_""")
									a__       :						Tuple													=  pipeline.text_encoder
						else:
									a__       :						str													=  key.split(""".""")[0].split(LORA_PREFIX_UNET + """_""")[-1].split("""_""")
									a__       :						str													=  pipeline.unet

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

						a__       :						List[str]													=  []
						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:
									a__       :						Optional[int]													=  state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.floataa)
									a__       :						Dict													=  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:
									a__       :						Dict													=  state_dict[pair_keys[0]].to(torch.floataa)
									a__       :						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__":
		_lowercase		:				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.)")

		_lowercase		:				Any      		=parser.parse_args()

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

		_lowercase		:				List[Any]      		=convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




