'''simple docstring'''




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Any				)    ->   str:
							# load base model
							lowercase_      :    List[str]       		= StableDiffusionPipeline.from_pretrained(UpperCAmelCase__   ,   torch_dtype=torch.floataa				)

							# load LoRA weight from .safetensors
							lowercase_      :    Optional[int]       		= load_file(UpperCAmelCase__				)

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

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

														lowercase_      :    int       		= []
														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:
																					lowercase_      :    str       		= state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
																					lowercase_      :    List[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:
																					lowercase_      :    Dict       		= state_dict[pair_keys[0]].to(torch.floataa				)
																					lowercase_      :    int       		= 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__":
						_lowercase		:				List[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=0.7_5, 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		:				List[str]	       =		parser.parse_args()

						_lowercase		:				Tuple	       =		args.base_model_path
						_lowercase		:				Dict	       =		args.checkpoint_path
						_lowercase		:				Union[str, Any]	       =		args.dump_path
						_lowercase		:				Dict	       =		args.lora_prefix_unet
						_lowercase		:				Tuple	       =		args.lora_prefix_text_encoder
						_lowercase		:				List[str]	       =		args.alpha

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

						_lowercase		:				str	       =		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_tokenizers_available, is_torch_available


_lowercase		:				Dict	       =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		["BloomTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Dict	       =		[
						    "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BloomForCausalLM",
						    "BloomModel",
						    "BloomPreTrainedModel",
						    "BloomForSequenceClassification",
						    "BloomForTokenClassification",
						    "BloomForQuestionAnswering",
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





