

'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None



class 					lowercase						:


							def __init__(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					None
								UpperCAmelCase_		:      Optional[int]       	=					None


							def __iter__(			self)							->				Iterator[Any]:
								UpperCAmelCase_		:      Optional[int]       	=					self.head
								while self.head:
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next
									if node == self.head:
										break


							def __len__(			self)							->				int:
								return sum(1 for _ in self)


							def __repr__(			self)							->				str:
								return "->".join(str(_snake_case) for item in iter(self))


							def 						_snake_case							(			self    ,			_snake_case)							->				None:
								self.insert_nth(len(self)    ,			_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				None:
								self.insert_nth(0    ,			_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				None:
								if index < 0 or index > len(self):
									raise IndexError('list index out of range.')
								UpperCAmelCase_		:      str       	=					Node(_snake_case)
								if self.head is None:
									UpperCAmelCase_		:      Union[str, Any]       	=					new_node  # first node points itself
									UpperCAmelCase_		:      List[str]       	=					new_node
								elif index == 0:  # insert at head
									UpperCAmelCase_		:      Optional[int]       	=					self.head
									UpperCAmelCase_		:      int       	=					new_node
								else:
									UpperCAmelCase_		:      List[Any]       	=					self.head
									for _ in range(index - 1):
										UpperCAmelCase_		:      Any       	=					temp.next
									UpperCAmelCase_		:      List[str]       	=					temp.next
									UpperCAmelCase_		:      List[Any]       	=					new_node
									if index == len(self) - 1:  # insert at tail
										UpperCAmelCase_		:      Optional[Any]       	=					new_node


							def 						_snake_case							(			self)							->				Any:
								return self.delete_nth(0)


							def 						_snake_case							(			self)							->				Any:
								return self.delete_nth(len(self) - 1)


							def 						_snake_case							(			self    ,			_snake_case = 0)							->				Any:
								if not 0 <= index < len(self):
									raise IndexError('list index out of range.')
								UpperCAmelCase_		:      Optional[int]       	=					self.head
								if self.head == self.tail:  # just one node
									UpperCAmelCase_		:      List[Any]       	=					None
								elif index == 0:  # delete head node
									UpperCAmelCase_		:      List[Any]       	=					self.tail.next.next
									UpperCAmelCase_		:      Union[str, Any]       	=					self.head.next
								else:
									UpperCAmelCase_		:      List[Any]       	=					self.head
									for _ in range(index - 1):
										UpperCAmelCase_		:      List[str]       	=					temp.next
									UpperCAmelCase_		:      Optional[int]       	=					temp.next
									UpperCAmelCase_		:      Union[str, Any]       	=					temp.next.next
									if index == len(self) - 1:  # delete at tail
										UpperCAmelCase_		:      Union[str, Any]       	=					temp
								return delete_node.data





							def 						_snake_case							(			self)							->				bool:
								return len(self) == 0







def 						SCREAMING_SNAKE_CASE(  )    ->    None:
	UpperCAmelCase_		:      Dict       	=					CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase   ,i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0   ,7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2   ,3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
						import doctest

						doctest.testmod()









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

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

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

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

		UpperCAmelCase_		:      str       	=					[]
		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:
			UpperCAmelCase_		:      Dict       	=					state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
			UpperCAmelCase_		:      List[str]       	=					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:
			UpperCAmelCase_		:      Optional[int]       	=					state_dict[pair_keys[0]].to(torch.floataa  )
			UpperCAmelCase_		:      Tuple       	=					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)






