



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Union[str, Any]:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = StableDiffusionPipeline.from_pretrained(__lowercase							,     torch_dtype=torch.floataa       )

						# load LoRA weight from .safetensors
						A__	:							Tuple						    = load_file(__lowercase       )

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

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

												A__	:							Tuple						    = []
												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:
																		A__	:							int						    = state_dict[pair_keys[0]].squeeze(3       ).squeeze(2       ).to(torch.floataa       )
																		A__	:							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:
																		A__	:							str						    = state_dict[pair_keys[0]].to(torch.floataa       )
																		A__	:							List[Any]						    = 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__":
		snake_case    :						int  =						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.)')

		snake_case    :						List[Any]  =						parser.parse_args()

		snake_case    :						Union[str, Any]  =						args.base_model_path
		snake_case    :						Optional[int]  =						args.checkpoint_path
		snake_case    :						Optional[int]  =						args.dump_path
		snake_case    :						Union[str, Any]  =						args.lora_prefix_unet
		snake_case    :						int  =						args.lora_prefix_text_encoder
		snake_case    :						Optional[Any]  =						args.alpha

		snake_case    :						Union[str, Any]  =						convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

		snake_case    :						Optional[int]  =						pipe.to(args.device)
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)