






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							int     ):
	snake_case__				:   str     =						StableDiffusionPipeline.from_pretrained(_UpperCamelCase  ,						torch_dtype=torch.floataa     )

	# load LoRA weight from .safetensors
	snake_case__				:   int     =						load_file(_UpperCamelCase     )

	snake_case__				:   List[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:
			snake_case__				:   int     =						key.split("."     )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"     )[-1].split("_"     )
			snake_case__				:   Any     =						pipeline.text_encoder
		else:
			snake_case__				:   Union[str, Any]     =						key.split("."     )[0].split(LORA_PREFIX_UNET + "_"     )[-1].split("_"     )
			snake_case__				:   Optional[Any]     =						pipeline.unet

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

		snake_case__				:   str     =						[]
		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:
			snake_case__				:   List[Any]     =						state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
			snake_case__				:   Union[str, Any]     =						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:
			snake_case__				:   List[str]     =						state_dict[pair_keys[0]].to(torch.floataa     )
			snake_case__				:   str     =						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__":
							__lowerCamelCase      :    Optional[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.)""")

							__lowerCamelCase      :    int   		=   parser.parse_args()

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

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

							__lowerCamelCase      :    str   		=   pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCamelCase_		)
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	field(default="text-classification"				,     metadata={"include_in_asdict_even_if_is_default": True}		)
					a_			=	Features({"text": Value("string"		)}		)
					a_			=	Features({"labels": ClassLabel}		)
					a_			=	"text"
					a_			=	"labels"



					def  _lowercase  (      self  :						Tuple							,	__A  :						List[Any] ):
						if self.label_column not in features:
							raise ValueError(f'''Column {self.label_column} is not present in features.''' )
						if not isinstance(features[self.label_column]							,	__A ):
							raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
						snake_case__				:   Any     =						copy.deepcopy(self )
						snake_case__				:   Optional[Any]     =						self.label_schema.copy()
						snake_case__				:   List[str]     =						features[self.label_column]
						snake_case__				:   Dict     =						label_schema
						return task_template






					@property
					def  _lowercase  (      self  :						Tuple ):
						return {
						    self.text_column: "text",
						    self.label_column: "labels",
						}



