





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    List[str]				=  StableDiffusionPipeline.from_pretrained(_UpperCamelCase ,				torch_dtype=torch.floataa )

				# load LoRA weight from .safetensors
				snake_case_						:    Dict				=  load_file(_UpperCamelCase )

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

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

								snake_case_						:    int				=  []
								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:
												snake_case_						:    Tuple				=  state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
												snake_case_						:    Dict				=  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:
												snake_case_						:    int				=  state_dict[pair_keys[0]].to(torch.floataa )
												snake_case_						:    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)







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      BioGptTokenizer
			lowerCamelCase_     :     Optional[Any]       			=      False






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_						:    Optional[Any]				=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''w</w>''',
							    '''r</w>''',
							    '''t</w>''',
							    '''lo''',
							    '''low''',
							    '''er</w>''',
							    '''low</w>''',
							    '''lowest</w>''',
							    '''newer</w>''',
							    '''wider</w>''',
							    '''<unk>''',
							]
							snake_case_						:    Union[str, Any]				=  dict(zip(__magic_name__					,							range(len(__magic_name__    )    )    )    )
							snake_case_						:    Union[str, Any]				=  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

							snake_case_						:    Any				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''vocab_file''']    )
							snake_case_						:    Dict				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''merges_file''']    )
							with open(self.vocab_file					,							'''w'''    ) as fp:
											fp.write(json.dumps(__magic_name__    )    )
							with open(self.merges_file					,							'''w'''    ) as fp:
											fp.write('''\n'''.join(__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    str				=  '''lower newer'''
							snake_case_						:    Dict				=  '''lower newer'''
							return input_text, output_text






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer(self.vocab_file					,							self.merges_file    )

							snake_case_						:    Union[str, Any]				=  '''lower'''
							snake_case_						:    Optional[int]				=  ['''low''', '''er</w>''']
							snake_case_						:    Any				=  tokenizer.tokenize(__magic_name__    )
							self.assertListEqual(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  tokens + ['''<unk>''']
							snake_case_						:    List[str]				=  [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__    )					,							__magic_name__    )






			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''    )

							snake_case_						:    List[Any]				=  tokenizer.encode('''sequence builders'''					,							add_special_tokens=__magic_name__    )
							snake_case_						:    str				=  tokenizer.encode('''multi-sequence build'''					,							add_special_tokens=__magic_name__    )

							snake_case_						:    str				=  tokenizer.build_inputs_with_special_tokens(__magic_name__    )
							snake_case_						:    List[str]				=  tokenizer.build_inputs_with_special_tokens(__magic_name__					,							__magic_name__    )

							self.assertTrue(encoded_sentence == [2] + text    )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )
