


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       a_ (       SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Tuple						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__						,     torch_dtype=torch.floataa	)

       # load LoRA weight from .safetensors
       _lowerCamelCase			:						Tuple            =load_file(SCREAMING_SNAKE_CASE__	)

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

              # find the target layer
              _lowerCamelCase			:						int            =layer_infos.pop(0	)
              while len(SCREAMING_SNAKE_CASE__	) > -1:
                     try:
                            _lowerCamelCase			:						Dict            =curr_layer.__getattr__(SCREAMING_SNAKE_CASE__	)
                            if len(SCREAMING_SNAKE_CASE__	) > 0:
                                   _lowerCamelCase			:						Any            =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:
                                   _lowerCamelCase			:						List[Any]            =layer_infos.pop(0	)

              _lowerCamelCase			:						Optional[Any]            =[]
              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:
                     _lowerCamelCase			:						Tuple            =state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
                     _lowerCamelCase			:						int            =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:
                     _lowerCamelCase			:						List[str]            =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(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=0.75, 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)






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets




class A    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

             def _map_to_encoder_decoder_inputs(lowercase_		:	Tuple					):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _lowerCamelCase			:						Optional[int]            =tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=512					)
                    _lowerCamelCase			:						List[str]            =tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=128					)
                    _lowerCamelCase			:						List[str]            =inputs.input_ids
                    _lowerCamelCase			:						Any            =inputs.attention_mask

                    _lowerCamelCase			:						List[str]            =outputs.input_ids
                    _lowerCamelCase			:						int            =outputs.input_ids.copy()
                    _lowerCamelCase			:						List[str]            =[
                        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                    ]
                    _lowerCamelCase			:						Dict            =outputs.attention_mask

                    assert all(len(lowercase_					) == 512 for x in inputs.input_ids					)
                    assert all(len(lowercase_					) == 128 for x in outputs.input_ids					)

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

             # map train dataset
             _lowerCamelCase			:						Tuple            =train_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             train_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             # same for validation dataset
             _lowerCamelCase			:						Tuple            =val_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             val_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


