

'''simple docstring'''





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 				_lowerCAmelCase			(   __magic_name__						:     Dict   , __magic_name__						:     List[str]   , __magic_name__						:     Any   , __magic_name__						:     str   , __magic_name__						:     str						)				->  Any:
						# load base model
						lowercase :							Any    		=StableDiffusionPipeline.from_pretrained(__magic_name__   , torch_dtype=torch.floataa						)

						# load LoRA weight from .safetensors
						lowercase :							Tuple    		=load_file(__magic_name__						)

						lowercase :							str    		=[]

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

												# find the target layer
												lowercase :							Dict    		=layer_infos.pop(0						)
												while len(__magic_name__						) > -1:
																		try:
																								lowercase :							Tuple    		=curr_layer.__getattr__(__magic_name__						)
																								if len(__magic_name__						) > 0:
																														lowercase :							Any    		=layer_infos.pop(0						)
																								elif len(__magic_name__						) == 0:
																														break
																		except Exception:
																								if len(__magic_name__						) > 0:
																														temp_name += "_" + layer_infos.pop(0						)
																								else:
																														lowercase :							int    		=layer_infos.pop(0						)

												lowercase :							Optional[Any]    		=[]
												if "lora_down" in key:
																		pair_keys.append(key.replace('''lora_down'''   , '''lora_up'''						)						)
																		pair_keys.append(__magic_name__						)
												else:
																		pair_keys.append(__magic_name__						)
																		pair_keys.append(key.replace('''lora_up'''   , '''lora_down'''						)						)

												# update weight
												if len(state_dict[pair_keys[0]].shape						) == 4:
																		lowercase :							Optional[int]    		=state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
																		lowercase :							Dict    		=state_dict[pair_keys[1]].squeeze(3						).squeeze(2						).to(torch.floataa						)
																		curr_layer.weight.data += alpha * torch.mm(__magic_name__   , __magic_name__						).unsqueeze(2						).unsqueeze(3						)
												else:
																		lowercase :							List[str]    		=state_dict[pair_keys[0]].to(torch.floataa						)
																		lowercase :							Union[str, Any]    		=state_dict[pair_keys[1]].to(torch.floataa						)
																		curr_layer.weight.data += alpha * torch.mm(__magic_name__   , __magic_name__						)

												# update visited list
												for item in pair_keys:
																		visited.append(__magic_name__						)

						return pipeline


if __name__ == "__main__":
							UpperCamelCase_    						=       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.)""")

							UpperCamelCase_    						=       parser.parse_args()

							UpperCamelCase_    						=       args.base_model_path
							UpperCamelCase_    						=       args.checkpoint_path
							UpperCamelCase_    						=       args.dump_path
							UpperCamelCase_    						=       args.lora_prefix_unet
							UpperCamelCase_    						=       args.lora_prefix_text_encoder
							UpperCamelCase_    						=       args.alpha

							UpperCamelCase_    						=       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

							UpperCamelCase_    						=       pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








'''simple docstring'''





import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    AlbertForMaskedLM,
							    AlbertForMultipleChoice,
							    AlbertForPreTraining,
							    AlbertForQuestionAnswering,
							    AlbertForSequenceClassification,
							    AlbertForTokenClassification,
							    AlbertModel,
							)
							from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Dict		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		str=13		,					UpperCAmelCase__     :		Tuple=7		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		Tuple=True		,					UpperCAmelCase__     :		Optional[Any]=99		,					UpperCAmelCase__     :		Any=16		,					UpperCAmelCase__     :		Optional[Any]=36		,					UpperCAmelCase__     :		str=6		,					UpperCAmelCase__     :		Any=6		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		Any=37		,					UpperCAmelCase__     :		List[str]="gelu"		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		Optional[int]=0.1		,					UpperCAmelCase__     :		List[str]=512		,					UpperCAmelCase__     :		List[str]=16		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		Any=0.02		,					UpperCAmelCase__     :		Union[str, Any]=3		,					UpperCAmelCase__     :		List[Any]=4		,					UpperCAmelCase__     :		Tuple=None		,					):
										'''simple docstring'''
										lowercase :							str    		=parent
										lowercase :							int    		=batch_size
										lowercase :							Any    		=seq_length
										lowercase :							int    		=is_training
										lowercase :							str    		=use_input_mask
										lowercase :							int    		=use_token_type_ids
										lowercase :							Dict    		=use_labels
										lowercase :							int    		=vocab_size
										lowercase :							str    		=embedding_size
										lowercase :							Union[str, Any]    		=hidden_size
										lowercase :							Tuple    		=num_hidden_layers
										lowercase :							Any    		=num_hidden_groups
										lowercase :							Union[str, Any]    		=num_attention_heads
										lowercase :							Any    		=intermediate_size
										lowercase :							Tuple    		=hidden_act
										lowercase :							Optional[int]    		=hidden_dropout_prob
										lowercase :							Union[str, Any]    		=attention_probs_dropout_prob
										lowercase :							List[Any]    		=max_position_embeddings
										lowercase :							int    		=type_vocab_size
										lowercase :							int    		=type_sequence_label_size
										lowercase :							Any    		=initializer_range
										lowercase :							List[Any]    		=num_labels
										lowercase :							int    		=num_choices
										lowercase :							Optional[int]    		=scope







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		=ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size   )

										lowercase :							Optional[int]    		=None
										if self.use_input_mask:
																lowercase :							List[str]    		=random_attention_mask([self.batch_size, self.seq_length]   )

										lowercase :							Dict    		=None
										if self.use_token_type_ids:
																lowercase :							List[str]    		=ids_tensor([self.batch_size, self.seq_length]		,					self.type_vocab_size   )

										lowercase :							Tuple    		=None
										lowercase :							Any    		=None
										lowercase :							Dict    		=None
										if self.use_labels:
																lowercase :							int    		=ids_tensor([self.batch_size]		,					self.type_sequence_label_size   )
																lowercase :							str    		=ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels   )
																lowercase :							Optional[Any]    		=ids_tensor([self.batch_size]		,					self.num_choices   )

										lowercase :							Any    		=self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return AlbertConfig(
										    vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					intermediate_size=self.intermediate_size		,					hidden_act=self.hidden_act		,					hidden_dropout_prob=self.hidden_dropout_prob		,					attention_probs_dropout_prob=self.attention_probs_dropout_prob		,					max_position_embeddings=self.max_position_embeddings		,					type_vocab_size=self.type_vocab_size		,					initializer_range=self.initializer_range		,					num_hidden_groups=self.num_hidden_groups		,					)







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										lowercase :							int    		=AlbertModel(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							int    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__   )
										lowercase :							Dict    		=model(UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__   )
										lowercase :							int    		=model(UpperCAmelCase__   )
										self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)   )
										self.parent.assertEqual(result.pooler_output.shape		,					(self.batch_size, self.hidden_size)   )







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Tuple    		=AlbertForPreTraining(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							int    		=model(
										    UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__		,					sentence_order_label=UpperCAmelCase__		,					)
										self.parent.assertEqual(result.prediction_logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)   )
										self.parent.assertEqual(result.sop_logits.shape		,					(self.batch_size, config.num_labels)   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							Tuple    		=AlbertForMaskedLM(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							str    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)   )







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=AlbertForQuestionAnswering(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							List[str]    		=model(
										    UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					start_positions=UpperCAmelCase__		,					end_positions=UpperCAmelCase__		,					)
										self.parent.assertEqual(result.start_logits.shape		,					(self.batch_size, self.seq_length)   )
										self.parent.assertEqual(result.end_logits.shape		,					(self.batch_size, self.seq_length)   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.num_labels
										lowercase :							Any    		=AlbertForSequenceClassification(UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							Dict    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.num_labels
										lowercase :							str    		=AlbertForTokenClassification(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							int    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.num_choices
										lowercase :							List[Any]    		=AlbertForMultipleChoice(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							List[Any]    		=input_ids.unsqueeze(1   ).expand(-1		,					self.num_choices		,					-1   ).contiguous()
										lowercase :							int    		=token_type_ids.unsqueeze(1   ).expand(-1		,					self.num_choices		,					-1   ).contiguous()
										lowercase :							int    		=input_mask.unsqueeze(1   ).expand(-1		,					self.num_choices		,					-1   ).contiguous()
										lowercase :							List[str]    		=model(
										    UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__		,					)
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.prepare_config_and_inputs()
										(
										    (
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	
										) :							Dict    		=config_and_inputs
										lowercase :							Optional[Any]    		={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





@require_torch
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    (
				    (
				        AlbertModel,
				        AlbertForPreTraining,
				        AlbertForMaskedLM,
				        AlbertForMultipleChoice,
				        AlbertForSequenceClassification,
				        AlbertForTokenClassification,
				        AlbertForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCamelCase_		       =    (
				    {
				        'feature-extraction': AlbertModel,
				        'fill-mask': AlbertForMaskedLM,
				        'question-answering': AlbertForQuestionAnswering,
				        'text-classification': AlbertForSequenceClassification,
				        'token-classification': AlbertForTokenClassification,
				        'zero-shot': AlbertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCamelCase_		       =    True







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		int=False   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=super()._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__		,					return_labels=UpperCAmelCase__   )

										if return_labels:
																if model_class in get_values(UpperCAmelCase__   ):
																						lowercase :							Any    		=torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length)		,					dtype=torch.long		,					device=UpperCAmelCase__   )
																						lowercase :							Any    		=torch.zeros(
																						    self.model_tester.batch_size		,					dtype=torch.long		,					device=UpperCAmelCase__   )
										return inputs_dict







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=AlbertModelTester(self   )
										lowercase :							Optional[Any]    		=ConfigTester(self		,					config_class=UpperCAmelCase__		,					hidden_size=37   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							int    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																lowercase :							Tuple    		=type
																self.model_tester.create_and_check_model(*UpperCAmelCase__   )







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase :							str    		=AlbertModel.from_pretrained(UpperCAmelCase__   )
																self.assertIsNotNone(UpperCAmelCase__   )





@require_torch
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							int    		=AlbertModel.from_pretrained('''albert-base-v2'''   )
										lowercase :							Optional[int]    		=torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
										lowercase :							Any    		=torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
										with torch.no_grad():
																lowercase :							Any    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__   )[0]
										lowercase :							int    		=torch.Size((1, 11, 768)   )
										self.assertEqual(output.shape		,					UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=torch.tensor(
										    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]   )

										self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,					UpperCAmelCase__		,					atol=1E-4   )   )





