



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  StableDiffusionPipeline.from_pretrained(__UpperCAmelCase ,torch_dtype=torch.floataa)

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

				lowerCAmelCase__  :       Tuple				=  []

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

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

								lowerCAmelCase__  :       Dict				=  []
								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__  :       Union[str, Any]				=  state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.floataa)
												lowerCAmelCase__  :       Tuple				=  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__  :       Tuple				=  state_dict[pair_keys[0]].to(torch.floataa)
												lowerCAmelCase__  :       Optional[int]				=  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__":
			__snake_case       :	Optional[Any]            =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       :	Any            =parser.parse_args()

			__snake_case       :	Any            =args.base_model_path
			__snake_case       :	int            =args.checkpoint_path
			__snake_case       :	List[Any]            =args.dump_path
			__snake_case       :	Any            =args.lora_prefix_unet
			__snake_case       :	Dict            =args.lora_prefix_text_encoder
			__snake_case       :	List[str]            =args.alpha

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

			__snake_case       :	List[str]            =pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

