def       lowerCAmelCase_     (		lowercase:							list[list]		):







 '''simple docstring'''
 _UpperCamelCase:     str							= current_set.copy()
 for row_index, row in enumerate(lowercase		):
  _UpperCamelCase:     Optional[Any]							= row[0]
  for column_index, column in enumerate(lowercase		):
   if magnitude == 0:
    _UpperCamelCase:     List[str]							= column
    continue
   _UpperCamelCase:     Optional[int]							= column / magnitude
    # Subtract to cancel term
 _UpperCamelCase:     List[str]							= current_set[0]
 _UpperCamelCase:     Dict							= [first_row]
 _UpperCamelCase:     Union[str, Any]							= current_set[1::]
 for row in current_set:
  _UpperCamelCase:     List[Any]							= []
  # If first term is 0, it is already in form we want, so we preserve it
  if row[0] == 0:
   final_set.append(lowercase		)
   continue
  for column_index in range(len(lowercase		)		):
   temp_row.append(first_row[column_index] - row[column_index]		)
  final_set.append(lowercase		)
 # Create next recursion iteration set
 if len(final_set[0]		) != 3:
  _UpperCamelCase:     Union[str, Any]							= final_set[0]
  _UpperCamelCase:     List[Any]							= []
  _UpperCamelCase:     Optional[int]							= []
  for row in final_set[1::]:
   current_first_column.append(row[0]		)
   next_iteration.append(row[1::]		)
  _UpperCamelCase:     str							= simplify(lowercase		)
  for i in range(len(lowercase		)		):
   resultant[i].insert(0  ,					current_first_column[i]		)
  resultant.insert(0  ,					lowercase		)
  _UpperCamelCase:     Optional[Any]							= resultant
 return final_set




def       lowerCAmelCase_     (		lowercase:							list[list]		):







 '''simple docstring'''
 if len(lowercase		) == 0:
  raise IndexError('''solve_simultaneous() requires n lists of length n+1'''		)
 _UpperCamelCase:     Optional[int]							= len(lowercase		) + 1
 if any(len(lowercase		) != _length for item in equations		):
  raise IndexError('''solve_simultaneous() requires n lists of length n+1'''		)
 for row in equations:
  if any(not isinstance(lowercase  ,					(int, float)		) for column in row		):
   raise ValueError('''solve_simultaneous() requires lists of integers'''		)
 if len(lowercase		) == 1:
  return [equations[0][-1] / equations[0][0]]
 _UpperCamelCase:     Tuple							= equations.copy()
 if any(0 in row for row in data_set		):
  _UpperCamelCase:     Union[str, Any]							= data_set.copy()
  _UpperCamelCase:     int							= []
  for row_index, row in enumerate(lowercase		):
   if 0 not in row:
    _UpperCamelCase:     List[str]							= data_set.pop(lowercase		)
    break
  if not full_row:
   raise ValueError('''solve_simultaneous() requires at least 1 full equation'''		)
  data_set.insert(0  ,					lowercase		)
 _UpperCamelCase:     Tuple							= data_set.copy()
 _UpperCamelCase:     List[Any]							= simplify(lowercase		)
 _UpperCamelCase:     Optional[Any]							= simplified[::-1]
 _UpperCamelCase:     list							= []
 for row in simplified:
  _UpperCamelCase:     int							= row[-1]
  if not solutions:
   if row[-2] == 0:
    solutions.append(0		)
    continue
   solutions.append(current_solution / row[-2]		)
   continue
  _UpperCamelCase:     Any							= row.copy()[: len(lowercase		) - 1 :]
  while temp_row[0] == 0:
   temp_row.pop(0		)
  if len(lowercase		) == 0:
   solutions.append(0		)
   continue
  _UpperCamelCase:     int							= temp_row[1::]
  _UpperCamelCase:     Optional[Any]							= temp_row[::-1]
  for column_index, column in enumerate(lowercase		):
   current_solution -= column * solutions[column_index]
  solutions.append(lowercase		)
 _UpperCamelCase:     List[str]							= []
 for item in solutions:
  final.append(float(round(lowercase  ,					5		)		)		)
 return final[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase_			    =							[
      [2, 1, 1, 1, 1, 4],
      [1, 2, 1, 1, 1, 5],
      [1, 1, 2, 1, 1, 6],
      [1, 1, 1, 2, 1, 7],
      [1, 1, 1, 1, 2, 8],
  ]
  print(solve_simultaneous(eq))
  print(solve_simultaneous([[4, 2]]))
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

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

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

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

  # update weight
  if len(state_dict[pair_keys[0]].shape		) == 4:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= state_dict[pair_keys[1]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		).unsqueeze(2		).unsqueeze(3		)
  else:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= state_dict[pair_keys[1]].to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		)

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

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							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=0.7_5, 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.)''')

  UpperCAmelCase_			    =							parser.parse_args()

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

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

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)