


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		Tuple      ,  a__:		str      ,  a__:		str      ,  a__:		List[Any]	)  ->		List[str]:


							'''simple docstring'''





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

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

							_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(lowerCamelCase__	) > -1:
																					try:
																												_UpperCAmelCase					=      curr_layer.__getattr__(lowerCamelCase__	)
																												if len(lowerCamelCase__	) > 0:
																																			_UpperCAmelCase					=      layer_infos.pop(0	)
																												elif len(lowerCamelCase__	) == 0:
																																			break
																					except Exception:
																												if len(lowerCamelCase__	) > 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(lowerCamelCase__	)
														else:
																					pair_keys.append(lowerCamelCase__	)
																					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(lowerCamelCase__      ,  lowerCamelCase__	).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(lowerCamelCase__      ,  lowerCamelCase__	)

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

							return pipeline


if __name__ == "__main__":
						lowerCAmelCase__  :Union[str, Any]             =							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__  :List[str]             =							parser.parse_args()

						lowerCAmelCase__  :int             =							args.base_model_path
						lowerCAmelCase__  :List[str]             =							args.checkpoint_path
						lowerCAmelCase__  :Tuple             =							args.dump_path
						lowerCAmelCase__  :int             =							args.lora_prefix_unet
						lowerCAmelCase__  :str             =							args.lora_prefix_text_encoder
						lowerCAmelCase__  :int             =							args.alpha

						lowerCAmelCase__  :List[Any]             =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						lowerCAmelCase__  :Any             =							pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





							if deriv:
														return value * (1 - value)
							return 1 / (1 + math.exp(-value	))


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
