



"""simple docstring"""





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def 	UpperCAmelCase     (		A	:						Union[str, Any]     ,				A	:						Dict     ,				A	:						int     ,				A	:						Union[str, Any]     ,				A	:						Union[str, Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		StableDiffusionPipeline.from_pretrained(A     ,				torch_dtype=torch.floataa       )

		# load LoRA weight from .safetensors
		_UpperCAmelCase							 =		load_file(A       )

		_UpperCAmelCase							 =		[]

		# 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:
						_UpperCAmelCase							 =		key.split('.'       )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'       )[-1].split('_'       )
						_UpperCAmelCase							 =		pipeline.text_encoder
				else:
						_UpperCAmelCase							 =		key.split('.'       )[0].split(LORA_PREFIX_UNET + '_'       )[-1].split('_'       )
						_UpperCAmelCase							 =		pipeline.unet

				# find the target layer
				_UpperCAmelCase							 =		layer_infos.pop(0       )
				while len(A       ) > -1:
						try:
								_UpperCAmelCase							 =		curr_layer.__getattr__(A       )
								if len(A       ) > 0:
										_UpperCAmelCase							 =		layer_infos.pop(0       )
								elif len(A       ) == 0:
										break
						except Exception:
								if len(A       ) > 0:
										temp_name += "_" + layer_infos.pop(0       )
								else:
										_UpperCAmelCase							 =		layer_infos.pop(0       )

				_UpperCAmelCase							 =		[]
				if "lora_down" in key:
						pair_keys.append(key.replace('lora_down'     ,				'lora_up'       )       )
						pair_keys.append(A       )
				else:
						pair_keys.append(A       )
						pair_keys.append(key.replace('lora_up'     ,				'lora_down'       )       )

				# update weight
				if len(state_dict[pair_keys[0]].shape       ) == 4:
						_UpperCAmelCase							 =		state_dict[pair_keys[0]].squeeze(3       ).squeeze(2       ).to(torch.floataa       )
						_UpperCAmelCase							 =		state_dict[pair_keys[1]].squeeze(3       ).squeeze(2       ).to(torch.floataa       )
						curr_layer.weight.data += alpha * torch.mm(A     ,				A       ).unsqueeze(2       ).unsqueeze(3       )
				else:
						_UpperCAmelCase							 =		state_dict[pair_keys[0]].to(torch.floataa       )
						_UpperCAmelCase							 =		state_dict[pair_keys[1]].to(torch.floataa       )
						curr_layer.weight.data += alpha * torch.mm(A     ,				A       )

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

		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)







"""simple docstring"""





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowercase  							=					logging.get_logger(__name__)


lowercase  							=					TypeVar('''DatasetType''', Dataset, IterableDataset)




def 	UpperCAmelCase     (		A	:						List[DatasetType]     ,				A	:						Optional[List[float]] = None     ,				A	:						Optional[int] = None     ,				A	:						Optional[DatasetInfo] = None     ,				A	:						Optional[NamedSplit] = None     ,				A	:						Literal["first_exhausted", "all_exhausted"] = "first_exhausted"     ,				):

		'''simple docstring'''


		from .arrow_dataset import Dataset
		from .iterable_dataset import IterableDataset

		if not datasets:
				raise ValueError('Unable to interleave an empty list of datasets.'       )
		for i, dataset in enumerate(A       ):
				if not isinstance(A     ,				(Dataset, IterableDataset)       ):
						if isinstance(A     ,				(DatasetDict, IterableDatasetDict)       ):
								if not dataset:
										raise ValueError(
										    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
										    'is an empty dataset dictionary.'       )
								raise ValueError(
								    f'Dataset at position {i} has at least one split: {list(A       )}\n'
								    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A       )       )}\']'       )
						raise ValueError(
						    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A       ).__name__}.'       )
				if i == 0:
						_UpperCAmelCase ,					_UpperCAmelCase							 =		(
						    (Dataset, IterableDataset) if isinstance(A     ,				A       ) else (IterableDataset, Dataset)
						)
				elif not isinstance(A     ,				A       ):
						raise ValueError(
						    f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
		if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
				raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'       )
		if dataset_type is Dataset:
				return _interleave_map_style_datasets(
				    A     ,				A     ,				A     ,				info=A     ,				split=A     ,				stopping_strategy=A       )
		else:
				return _interleave_iterable_datasets(
				    A     ,				A     ,				A     ,				info=A     ,				split=A     ,				stopping_strategy=A       )






def 	UpperCAmelCase     (		A	:						List[DatasetType]     ,				A	:						Optional[DatasetInfo] = None     ,				A	:						Optional[NamedSplit] = None     ,				A	:						int = 0     ,				):

		'''simple docstring'''


		if not dsets:
				raise ValueError('Unable to concatenate an empty list of datasets.'       )
		for i, dataset in enumerate(A       ):
				if not isinstance(A     ,				(Dataset, IterableDataset)       ):
						if isinstance(A     ,				(DatasetDict, IterableDatasetDict)       ):
								if not dataset:
										raise ValueError(
										    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
										    'is an empty dataset dictionary.'       )
								raise ValueError(
								    f'Dataset at position {i} has at least one split: {list(A       )}\n'
								    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A       )       )}\']'       )
						raise ValueError(
						    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A       ).__name__}.'       )
				if i == 0:
						_UpperCAmelCase ,					_UpperCAmelCase							 =		(
						    (Dataset, IterableDataset) if isinstance(A     ,				A       ) else (IterableDataset, Dataset)
						)
				elif not isinstance(A     ,				A       ):
						raise ValueError(
						    f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
		if dataset_type is Dataset:
				return _concatenate_map_style_datasets(A     ,				info=A     ,				split=A     ,				axis=A       )
		else:
				return _concatenate_iterable_datasets(A     ,				info=A     ,				split=A     ,				axis=A       )


