






"""simple docstring"""







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       Tuple:
				# load base model
				lowercase    :      Optional[Any]       = StableDiffusionPipeline.from_pretrained(_A  ,				torch_dtype=torch.floataa )

				# load LoRA weight from .safetensors
				lowercase    :      int       = load_file(_A )

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

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

								lowercase    :      Dict       = []
								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:
												lowercase    :      Union[str, Any]       = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
												lowercase    :      str       = 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:
												lowercase    :      str       = state_dict[pair_keys[0]].to(torch.floataa )
												lowercase    :      List[str]       = 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__":
				_lowerCAmelCase            =	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.)')

				_lowerCAmelCase            =	parser.parse_args()

				_lowerCAmelCase            =	args.base_model_path
				_lowerCAmelCase            =	args.checkpoint_path
				_lowerCAmelCase            =	args.dump_path
				_lowerCAmelCase            =	args.lora_prefix_unet
				_lowerCAmelCase            =	args.lora_prefix_text_encoder
				_lowerCAmelCase            =	args.alpha

				_lowerCAmelCase            =	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

				_lowerCAmelCase            =	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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase            =	{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				_lowerCAmelCase            =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
