



'''simple docstring'''




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def        lowerCAmelCase_     (			_lowerCamelCase:     Any ,   _lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Optional[Any] ,   _lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     Optional[int]      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	StableDiffusionPipeline.from_pretrained(UpperCAmelCase__ ,   torch_dtype=torch.floataa      )

		# load LoRA weight from .safetensors
		__SCREAMING_SNAKE_CASE :	str					 =	load_file(UpperCAmelCase__      )

		__SCREAMING_SNAKE_CASE :	Dict					 =	[]

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

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

				__SCREAMING_SNAKE_CASE :	List[Any]					 =	[]
				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:
						__SCREAMING_SNAKE_CASE :	Tuple					 =	state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
						__SCREAMING_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:
						__SCREAMING_SNAKE_CASE :	List[str]					 =	state_dict[pair_keys[0]].to(torch.floataa      )
						__SCREAMING_SNAKE_CASE :	Any					 =	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__":
						UpperCamelCase__ :			int 						=				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.)''')

						UpperCamelCase__ :			Tuple 						=				parser.parse_args()

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

						UpperCamelCase__ :			Optional[int] 						=				convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						UpperCamelCase__ :			List[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_torch_available


UpperCamelCase__ :			List[Any]												=				{
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			List[Any]												=				[
						    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TimeSeriesTransformerForPrediction''',
						    '''TimeSeriesTransformerModel''',
						    '''TimeSeriesTransformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_time_series_transformer import (
						    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TimeSeriesTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_time_series_transformer import (
												    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TimeSeriesTransformerForPrediction,
												    TimeSeriesTransformerModel,
												    TimeSeriesTransformerPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			Dict												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)