


'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def 			_a			(		_lowercase      :			Any    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			int    ,      _lowercase      :			Tuple    ,      _lowercase      :			str						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    StableDiffusionPipeline.from_pretrained(_lowercase    ,      torch_dtype=torch.floataa						)

     # load LoRA weight from .safetensors
     __UpperCAmelCase			:				Optional[int]													=    load_file(_lowercase						)

     __UpperCAmelCase			:				List[str]													=    []

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

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

          __UpperCAmelCase			:				List[str]													=    []
          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			:				Any													=    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			:				Any													=    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    :Optional[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.)")

      __UpperCAmelCase    :Optional[Any]				       =     parser.parse_args()

      __UpperCAmelCase    :Dict				       =     args.base_model_path
      __UpperCAmelCase    :int				       =     args.checkpoint_path
      __UpperCAmelCase    :Optional[int]				       =     args.dump_path
      __UpperCAmelCase    :List[Any]				       =     args.lora_prefix_unet
      __UpperCAmelCase    :Tuple				       =     args.lora_prefix_text_encoder
      __UpperCAmelCase    :int				       =     args.alpha

      __UpperCAmelCase    :Dict				       =     convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

      __UpperCAmelCase    :Dict				       =     pipe.to(args.device)
      pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    len(_lowercase						)
     while cur > 1:
          # Find the maximum number in arr
          __UpperCAmelCase			:				Union[str, Any]													=    arr.index(max(arr[0:cur]						)						)
          # Reverse from 0 to mi
          __UpperCAmelCase			:				Any													=    arr[mi::-1] + arr[mi + 1 : len(_lowercase						)]
          # Reverse whole list
          __UpperCAmelCase			:				List[str]													=    arr[cur - 1 :: -1] + arr[cur : len(_lowercase						)]
          cur -= 1
     return arr


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     input("Enter numbers separated by a comma:\n").strip()
      __UpperCAmelCase    :Optional[int]				       =     [int(item) for item in user_input.split(",")]
      print(pancake_sort(unsorted))