


'''simple docstring'''






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def lowerCamelCase__     (		__lowerCamelCase			:				Union[str, Any]			,       __lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				int			,       __lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Any				):







   '''simple docstring'''




   _UpperCAmelCase  :  str				       =StableDiffusionPipeline.from_pretrained(__lowerCamelCase			,       torch_dtype=torch.floataa				)

   # load LoRA weight from .safetensors
   _UpperCAmelCase  :  Tuple				       =load_file(__lowerCamelCase				)

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

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

      _UpperCAmelCase  :  Any				       =[]
      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  :  int				       =state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
         _UpperCAmelCase  :  Any				       =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  :  str				       =state_dict[pair_keys[0]].to(torch.floataa				)
         _UpperCAmelCase  :  List[str]				       =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__":
 lowercase				=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.)')

 lowercase				=parser.parse_args()

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

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

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





'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

lowercase				=[8, 5, 9, 7]
lowercase				=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase				=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class    __magic_name__				:






       def __init__(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =claim_vector
          _UpperCAmelCase  :  Optional[int]				       =allocated_resources_table
          _UpperCAmelCase  :  int				       =maximum_claim_table






       def      lowerCAmelCase						(     self)		->    list[int]:




          '''simple docstring'''
          return [
              sum(p_item[i] for p_item in self.__allocated_resources_table)
              for i in range(len(self.__allocated_resources_table[0]))
          ]






       def      lowerCAmelCase						(     self)		->    list[int]:




          '''simple docstring'''
          return np.array(self.__claim_vector) - np.array(
              self.__processes_resource_summation())






       def      lowerCAmelCase						(     self)		->    list[list[int]]:




          '''simple docstring'''
          return [
              list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case))
              for i, allocated_resource in enumerate(self.__allocated_resources_table)
          ]






       def      lowerCAmelCase						(     self)		->    dict[int, list[int]]:




          '''simple docstring'''
          return {self.__need().index(snake_case): i for i in self.__need()}






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    None:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =self.__need()
          _UpperCAmelCase  :  str				       =self.__allocated_resources_table
          _UpperCAmelCase  :  Dict				       =self.__available_resources()
          _UpperCAmelCase  :  Tuple				       =self.__need_index_manager()
          for kw, val in kwargs.items():
             if kw and val is True:
                self.__pretty_data()
          print('_' * 5_0 + '\n')
          while need_list:
             _UpperCAmelCase  :  Any				       =False
             for each_need in need_list:
                _UpperCAmelCase  :  Any				       =True
                for index, need in enumerate(snake_case):
                   if need > available_resources[index]:
                      _UpperCAmelCase  :  Optional[int]				       =False
                      break
                if execution:
                   _UpperCAmelCase  :  Union[str, Any]				       =True
                   # get the original index of the process from ind_ctrl db
                   for original_need_index, need_clone in need_index_manager.items():
                      if each_need == need_clone:
                         _UpperCAmelCase  :  List[str]				       =original_need_index
                   print(f"Process {process_number + 1} is executing.")
                   # remove the process run from stack
                   need_list.remove(snake_case)
                   # update available/freed resources stack
                   _UpperCAmelCase  :  Any				       =np.array(snake_case) + np.array(
                       alloc_resources_table[process_number])
                   print(
                       'Updated available resource stack for processes: '
                       + ' '.join([str(snake_case) for x in available_resources]))
                   break
             if safe:
                print('The process is in a safe state.\n')
             else:
                print('System in unsafe state. Aborting...\n')
                break






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          print(' ' * 9 + 'Allocated Resource Table')
          for item in self.__allocated_resources_table:
             print(
                 f"P{self.__allocated_resources_table.index(snake_case) + 1}"
                 + ' '.join(f"{it:>8}" for it in item)
                 + '\n')
          print(' ' * 9 + 'System Resource Table')
          for item in self.__maximum_claim_table:
             print(
                 f"P{self.__maximum_claim_table.index(snake_case) + 1}"
                 + ' '.join(f"{it:>8}" for it in item)
                 + '\n')
          print(
              'Current Usage by Active Processes: '
              + ' '.join(str(snake_case) for x in self.__claim_vector))
          print(
              'Initial Available Resources:       '
              + ' '.join(str(snake_case) for x in self.__available_resources()))
          time.sleep(1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

