




"""simple docstring"""






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				# load base model
				_lowerCamelCase	:     List[Any]       =       StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_				,       torch_dtype=torch.floataa						)

				# load LoRA weight from .safetensors
				_lowerCamelCase	:     List[str]       =       load_file(SCREAMING_SNAKE_CASE_						)

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

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

								_lowerCamelCase	:     Dict       =       []
								if "lora_down" in key:
												pair_keys.append(key.replace('lora_down'				,       'lora_up'						)						)
												pair_keys.append(SCREAMING_SNAKE_CASE_						)
								else:
												pair_keys.append(SCREAMING_SNAKE_CASE_						)
												pair_keys.append(key.replace('lora_up'				,       'lora_down'						)						)

								# update weight
								if len(state_dict[pair_keys[0]].shape						) == 4:
												_lowerCamelCase	:     Any       =       state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
												_lowerCamelCase	:     Dict       =       state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
												curr_layer.weight.data += alpha * torch.mm(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_						).unsqueeze(2						).unsqueeze(3						)
								else:
												_lowerCamelCase	:     Optional[Any]       =       state_dict[pair_keys[0]].to(torch.floataa						)
												_lowerCamelCase	:     Union[str, Any]       =       state_dict[pair_keys[1]].to(torch.floataa						)
												curr_layer.weight.data += alpha * torch.mm(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_						)

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

				return pipeline


if __name__ == "__main__":
			lowercase__			=		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__			=		parser.parse_args()

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

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

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

from __future__ import annotations




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	float		,   SCREAMING_SNAKE_CASE_	:	float		,   SCREAMING_SNAKE_CASE_	:	float		,   )  ->     tuple:
						if (electron_conc, hole_conc, intrinsic_conc).count(0       ) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""       )
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""       )
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""       )
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""       )
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
		import doctest

		doctest.testmod()