




'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							_a			  =       StableDiffusionPipeline.from_pretrained(UpperCamelCase				,		torch_dtype=torch.floataa   )

							# load LoRA weight from .safetensors
							_a			  =       load_file(UpperCamelCase   )

							_a			  =       []

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

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

														_a			  =       []
														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:
																					_a			  =       state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
																					_a			  =       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:
																					_a			  =       state_dict[pair_keys[0]].to(torch.floataa   )
																					_a			  =       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__":
				_snake_case   :   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=0.75, 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.)')

				_snake_case   :   List[str]     							=	parser.parse_args()

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

				_snake_case   :   Union[str, Any]     							=	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

				_snake_case   :   Optional[Any]     							=	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_flax_available,
    is_torch_available,
    is_vision_available,
)


_snake_case   :   List[Any]     							=	{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Any     							=	['BeitFeatureExtractor']
				_snake_case   :   Any     							=	['BeitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BeitForImageClassification',
				    'BeitForMaskedImageModeling',
				    'BeitForSemanticSegmentation',
				    'BeitModel',
				    'BeitPreTrainedModel',
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'FlaxBeitForImageClassification',
				    'FlaxBeitForMaskedImageModeling',
				    'FlaxBeitModel',
				    'FlaxBeitPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_beit import BeitFeatureExtractor
								from .image_processing_beit import BeitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_beit import (
								    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BeitForImageClassification,
								    BeitForMaskedImageModeling,
								    BeitForSemanticSegmentation,
								    BeitModel,
								    BeitPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_beit import (
								    FlaxBeitForImageClassification,
								    FlaxBeitForMaskedImageModeling,
								    FlaxBeitModel,
								    FlaxBeitPreTrainedModel,
								)


else:
				import sys

				_snake_case   :   Union[str, Any]     							=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

