

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Optional[Any]      )			->      List[Any]:




				'''simple docstring'''





				A__        =			StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_	,       torch_dtype=torch.floataa      )

				# load LoRA weight from .safetensors
				A__        =			load_file(SCREAMING_SNAKE_CASE_      )

				A__        =			[]

				# 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:
												A__        =			key.split("."      )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"      )[-1].split("_"      )
												A__        =			pipeline.text_encoder
								else:
												A__        =			key.split("."      )[0].split(LORA_PREFIX_UNET + "_"      )[-1].split("_"      )
												A__        =			pipeline.unet

								# find the target layer
								A__        =			layer_infos.pop(0      )
								while len(SCREAMING_SNAKE_CASE_      ) > -1:
												try:
																A__        =			curr_layer.__getattr__(SCREAMING_SNAKE_CASE_      )
																if len(SCREAMING_SNAKE_CASE_      ) > 0:
																				A__        =			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:
																				A__        =			layer_infos.pop(0      )

								A__        =			[]
								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:
												A__        =			state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
												A__        =			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:
												A__        =			state_dict[pair_keys[0]].to(torch.floataa      )
												A__        =			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__":
	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=0.7_5, 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)




lowerCAmelCase__	   = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							bytes      )			->      bytes:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			F'a bytes-like object is required, not \'{data.__class__.__name__}\''
								raise TypeError(SCREAMING_SNAKE_CASE_      )

				A__        =			"".join(bin(SCREAMING_SNAKE_CASE_      )[2:].zfill(8      ) for byte in data      )

				A__        =			len(SCREAMING_SNAKE_CASE_      ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								A__        =			b"=" * ((6 - len(SCREAMING_SNAKE_CASE_      ) % 6) // 2)

								# Append binary_stream with arbitrary binary digits (0's by default) to make its
								# length a multiple of 6.
								binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE_      ) % 6)
				else:
								A__        =			b""

				# Encode every 6 binary digits to their corresponding Base64 character
				return (
				    "".join(
				        B64_CHARSET[int(binary_stream[index : index + 6]	,       2      )]
				        for index in range(0	,       len(SCREAMING_SNAKE_CASE_      )	,       6      )      ).encode()
				    + padding
				)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str      )			->      bytes:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ) and not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			(
								    "argument should be a bytes-like object or ASCII string, "
								    F'not \'{encoded_data.__class__.__name__}\''
								)
								raise TypeError(SCREAMING_SNAKE_CASE_      )

				# In case encoded_data is a bytes-like object, make sure it contains only
				# ASCII characters so we convert it to a string object
				if isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								try:
												A__        =			encoded_data.decode("utf-8"      )
								except UnicodeDecodeError:
												raise ValueError("base64 encoded data should only contain ASCII characters"      )

				A__        =			encoded_data.count("="      )

				# Check if the encoded string contains non base64 characters
				if padding:
								assert all(
								    char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
				else:
								assert all(
								    char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

				# Check the padding
				assert len(SCREAMING_SNAKE_CASE_      ) % 4 == 0 and padding < 3, "Incorrect padding"

				if padding:
								# Remove padding if there is one
								A__        =			encoded_data[:-padding]

								A__        =			"".join(
								    bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE_      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
				else:
								A__        =			"".join(
								    bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE_      )      )[2:].zfill(6      ) for char in encoded_data      )

				A__        =			[
				    int(binary_stream[index : index + 8]	,       2      )
				    for index in range(0	,       len(SCREAMING_SNAKE_CASE_      )	,       8      )
				]

				return bytes(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

