



"""simple docstring"""
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Tuple						=			StableDiffusionPipeline.from_pretrained(_lowercase,					torch_dtype=torch.floataa	)

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

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

												# find the target layer
												snake_case_ :List[Any]						=			layer_infos.pop(0	)
												while len(_lowercase	) > -1:
																		try:
																								snake_case_ :List[Any]						=			curr_layer.__getattr__(_lowercase	)
																								if len(_lowercase	) > 0:
																														snake_case_ :Dict						=			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_ :str						=			state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
																		snake_case_ :Tuple						=			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_ :List[str]						=			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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__a      =				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["Mask2FormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Mask2FormerForUniversalSegmentation",
						    "Mask2FormerModel",
						    "Mask2FormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_maskaformer import MaskaFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskaformer import (
												    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskaFormerForUniversalSegmentation,
												    MaskaFormerModel,
												    MaskaFormerPreTrainedModel,
												)


else:
						import sys

						__a      =				_LazyModule(__name__, globals()["__file__"], _import_structure)
