
'''simple docstring'''
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE,		torch_dtype=torch.floataa       )

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

    snake_case_ :    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:
            snake_case_ :    List[str]     			=						key.split("."       )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"       )[-1].split("_"       )
            snake_case_ :    int     			=						pipeline.text_encoder
        else:
            snake_case_ :    str     			=						key.split("."       )[0].split(LORA_PREFIX_UNET + "_"       )[-1].split("_"       )
            snake_case_ :    int     			=						pipeline.unet

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

        snake_case_ :    Optional[Any]     			=						[]
        if "lora_down" in key:
            pair_keys.append(key.replace("lora_down",		"lora_up"       )       )
            pair_keys.append(__SCREAMING_SNAKE_CASE       )
        else:
            pair_keys.append(__SCREAMING_SNAKE_CASE       )
            pair_keys.append(key.replace("lora_up",		"lora_down"       )       )

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

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

    return pipeline


if __name__ == "__main__":
  a_											=						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.)")

  a_											=						parser.parse_args()

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

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

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






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





 def __repr__(    self):
     return (
         F'Node: key: {self.key}, val: {self.val}, '
         F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
     )






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

     # All nodes other than self.head are guaranteed to have non-None previous
     assert previous is not None

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

     if node.prev is None or node.next is None:
         return None

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						self.list.remove(self.cache[key])
         assert node == value_node

         # node is guaranteed not None because it is in self.cache
         assert node is not None
         self.list.add(lowercase_)
         return node.val
     self.miss += 1
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						self.list.head.next

             # guaranteed to have a non-None first node when num_keys > 0
             # explain to type checker via assertions
             assert first_node is not None
             assert first_node.key is not None
             assert (
                 self.list.remove(lowercase_) is not None
             )  # node guaranteed to be in list assert node.key is not None

             del self.cache[first_node.key]
             self.num_keys -= 1
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

     else:
         # bump node to the end of the list, update value
         snake_case_ :    List[Any]     			=						self.list.remove(self.cache[key])
         assert node is not None  # node guaranteed to be in list
         snake_case_ :    Optional[Any]     			=						value
         self.list.add(lowercase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						func(*lowercase_)
                 cls.decorator_function_to_instance_map[func].put(args[0] ,       lowercase_)
             return result

         def cache_info() -> LRUCache[T, U]:
             return cls.decorator_function_to_instance_map[func]

         setattr(lowercase_ ,       "cache_info" ,       lowercase_)  # noqa: B010

         return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




