




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	List[Any]:
  # load base model
  lowerCAmelCase__				:  Any		       =     StableDiffusionPipeline.from_pretrained(UpperCamelCase	,      torch_dtype=torch.floataa      )

  # load LoRA weight from .safetensors
  lowerCAmelCase__				:  Tuple		       =     load_file(UpperCamelCase      )

  lowerCAmelCase__				:  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:
      lowerCAmelCase__				:  int		       =     key.split('''.'''      )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''      )[-1].split('''_'''      )
      lowerCAmelCase__				:  Any		       =     pipeline.text_encoder
    else:
      lowerCAmelCase__				:  Any		       =     key.split('''.'''      )[0].split(LORA_PREFIX_UNET + '''_'''      )[-1].split('''_'''      )
      lowerCAmelCase__				:  Dict		       =     pipeline.unet

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

    lowerCAmelCase__				:  Any		       =     []
    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:
      lowerCAmelCase__				:  int		       =     state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
      lowerCAmelCase__				:  Optional[Any]		       =     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:
      lowerCAmelCase__				:  Optional[Any]		       =     state_dict[pair_keys[0]].to(torch.floataa      )
      lowerCAmelCase__				:  Dict		       =     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__":
     lowerCAmelCase_					    = 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.)""")

     lowerCAmelCase_					    = parser.parse_args()

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

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

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







import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

