



import gc
import threading
import time

import psutil
import torch







class     lowerCAmelCase_		:
       def __init__(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     psutil.Process()
          UpperCamelCase   :  int						     =     False
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Union[str, Any]						     =     -1

          while True:
             UpperCamelCase   :  Tuple						     =     max(self.process.memory_info().rss,      self.cpu_memory_peak	)

             # can't sleep or will not catch the peak right (this comment is here on purpose)
             if not self.peak_monitoring:
                break
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Any						     =     True
          UpperCamelCase   :  Any						     =     threading.Thread(target=self.peak_monitor	)
          UpperCamelCase   :  List[str]						     =     True
          self.thread.start()






       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Any						     =     False
          self.thread.join()
          return self.cpu_memory_peak






__UpperCAmelCase	       =	PeakCPUMemory()
def 		UpperCamelCase     (       )     ->					List[Any]:
   # Time
   UpperCamelCase   :  Optional[int]						     =     {"time": time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   UpperCamelCase   :  Any						     =     psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()     ):
      UpperCamelCase   :  List[Any]						     =     torch.cuda.memory_allocated(_lowerCAmelCase     )
   torch.cuda.reset_peak_memory_stats()

   return measures
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					int:
   # Time
   UpperCamelCase   :  Optional[int]						     =     {"time": time.time() - start_measures["time"]}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   UpperCamelCase   :  str						     =     (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
   UpperCamelCase   :  Optional[int]						     =     (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()     ):
      UpperCamelCase   :  Union[str, Any]						     =     (torch.cuda.memory_allocated(_lowerCAmelCase     ) - start_measures[str(_lowerCAmelCase     )]) / 2**20
      UpperCamelCase   :  List[Any]						     =     (torch.cuda.max_memory_allocated(_lowerCAmelCase     ) - start_measures[str(_lowerCAmelCase     )]) / 2**20

   return measures


def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   List[Any]     )     ->					Any:
   print(F"""{description}:"""     )
   print(F"""- Time: {measures["time"]:.2f}s"""     )
   for i in range(torch.cuda.device_count()     ):
      print(F"""- GPU {i} allocated: {measures[str(_lowerCAmelCase     )]:.2f}MiB"""     )
      UpperCamelCase   :  Dict						     =     measures[F"""{i}-peak"""]
      print(F"""- GPU {i} peak: {peak:.2f}MiB"""     )
   print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB"""     )
   print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB"""     )








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

   # load LoRA weight from .safetensors
   UpperCamelCase   :  Union[str, Any]						     =     load_file(snake_case__     )

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

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

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

      # update weight
      if len(state_dict[pair_keys[0]].shape     ) == 4:
         UpperCamelCase   :  Dict						     =     state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         UpperCamelCase   :  Union[str, Any]						     =     state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     ).unsqueeze(2     ).unsqueeze(3     )
      else:
         UpperCamelCase   :  Dict						     =     state_dict[pair_keys[0]].to(torch.floataa     )
         UpperCamelCase   :  List[Any]						     =     state_dict[pair_keys[1]].to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     )

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

   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=0.75, 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.)''')

    __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)



