



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Optional[int]					):






 '''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__":
     A_		:     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.)''')

     A_		:     Tuple        =     parser.parse_args()

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

     A_		:     Optional[int]        =     convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

     A_		:     List[str]        =     pipe.to(args.device)
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






A_		:     Any        =     [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_		:     Tuple        =     [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_		:     Optional[int]        =     {
    0: '''Sunday''',
    1: '''Monday''',
    2: '''Tuesday''',
    3: '''Wednesday''',
    4: '''Thursday''',
    5: '''Friday''',
    6: '''Saturday''',
}


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 assert len(str(UpperCAmelCase__					)					) > 2, "year should be in YYYY format"
 assert 1 <= month <= 12, "month should be between 1 to 12"
 assert 1 <= day <= 31, "day should be between 1 to 31"

 # Doomsday algorithm:
 a						=		year // 1_00
 a						=		(5 * (century % 4) + 2) % 7
 a						=		year % 1_00
 a						=		centurian % 12
 a						=		(
     (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
 ) % 7
 a						=		(
     DOOMSDAY_NOT_LEAP[month - 1]
     if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
     else DOOMSDAY_LEAP[month - 1]
 )
 a						=		(dooms_day + day - day_anchor) % 7
 return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

