





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				snake_case_						:   List[str]           =  StableDiffusionPipeline.from_pretrained(_UpperCamelCase			,    torch_dtype=torch.floataa			)

				# load LoRA weight from .safetensors
				snake_case_						:   int           =  load_file(_UpperCamelCase			)

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

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

								snake_case_						:   Optional[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:
												snake_case_						:   Tuple           =  state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
												snake_case_						:   int           =  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:
												snake_case_						:   Dict           =  state_dict[pair_keys[0]].to(torch.floataa			)
												snake_case_						:   Optional[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__":
	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)







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  xgboost(_UpperCamelCase			,    _UpperCamelCase			)

				# Display the confusion matrix of the classifier with both training and test sets
				ConfusionMatrixDisplay.from_estimator(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    display_labels=_UpperCamelCase			,    cmap='''Blues'''			,    normalize='''true'''			,    )
				plt.title('''Normalized Confusion Matrix - IRIS Dataset'''			)
				plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()
