



import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCAmelCase	       =	16
__UpperCAmelCase	       =	32
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Optional[Any]:
   return int(x / 2**20     )



class     lowerCAmelCase_		:
       def __enter__(      self	)					->		List[str]:
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          UpperCamelCase   :  List[Any]						     =     torch.cuda.memory_allocated()
          return self






       def __exit__(      self,      *SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          gc.collect()
          torch.cuda.empty_cache()
          UpperCamelCase   :  List[str]						     =     torch.cuda.memory_allocated()
          UpperCamelCase   :  Dict						     =     torch.cuda.max_memory_allocated()
          UpperCamelCase   :  str						     =     bamb(self.end - self.begin	)
          UpperCamelCase   :  List[str]						     =     bamb(self.peak - self.begin	)
          # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 		UpperCamelCase     (       snake_case__	:   Accelerator	,					snake_case__	:   int = 16	,					snake_case__	:   str = "bert-base-cased"	,					snake_case__	:   int = 320	,					snake_case__	:   int = 160	,					)     ->					List[str]:
   UpperCamelCase   :  Optional[int]						     =     AutoTokenizer.from_pretrained(snake_case__     )
   UpperCamelCase   :  Union[str, Any]						     =     load_dataset(
       'glue'	,					'mrpc'	,					split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}     )

   def tokenize_function(snake_case__	:   Tuple     ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase   :  List[str]						     =     tokenizer(examples['sentence1']	,					examples['sentence2']	,					truncation=snake_case__	,					max_length=snake_case__     )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   UpperCamelCase   :  Dict						     =     datasets.map(
       snake_case__	,					batched=snake_case__	,					remove_columns=['idx', 'sentence1', 'sentence2']	,					load_from_cache_file=snake_case__     )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   UpperCamelCase   :  List[str]						     =     tokenized_datasets.rename_column('label'	,					'labels'     )

   def collate_fn(snake_case__	:   str     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(snake_case__	,					padding='max_length'	,					max_length=128	,					return_tensors='pt'     )
      return tokenizer.pad(snake_case__	,					padding='longest'	,					return_tensors='pt'     )

   # Instantiate dataloaders.
   UpperCamelCase   :  List[Any]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  List[str]						     =     DataLoader(
       tokenized_datasets['validation']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )

   return train_dataloader, eval_dataloader
def 		UpperCamelCase     (       snake_case__	:   Any	,					snake_case__	:   int     )     ->					Optional[int]:
   # Initialize accelerator
   UpperCamelCase   :  Optional[int]						     =     Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase   :  List[Any]						     =     config['lr']
   UpperCamelCase   :  List[str]						     =     int(config['num_epochs']     )
   UpperCamelCase   :  Tuple						     =     int(config['seed']     )
   UpperCamelCase   :  Tuple						     =     int(config['batch_size']     )
   UpperCamelCase   :  Union[str, Any]						     =     args.model_name_or_path

   set_seed(snake_case__     )
   UpperCamelCase     ,					UpperCamelCase   :  str						     =     get_dataloaders(snake_case__	,					snake_case__	,					snake_case__	,					args.n_train	,					args.n_val     )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase   :  str						     =     AutoModelForSequenceClassification.from_pretrained(snake_case__	,					return_dict=snake_case__     )

   # Instantiate optimizer
   UpperCamelCase   :  Any						     =     (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   UpperCamelCase   :  str						     =     optimizer_cls(params=model.parameters()	,					lr=snake_case__     )

   if accelerator.state.deepspeed_plugin is not None:
      UpperCamelCase   :  Tuple						     =     accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      UpperCamelCase   :  Any						     =     1
   UpperCamelCase   :  Dict						     =     (len(snake_case__     ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      UpperCamelCase   :  Dict						     =     get_linear_schedule_with_warmup(
          optimizer=snake_case__	,					num_warmup_steps=0	,					num_training_steps=snake_case__	,					)
   else:
      UpperCamelCase   :  List[str]						     =     DummyScheduler(snake_case__	,					total_num_steps=snake_case__	,					warmup_num_steps=0     )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  int						     =     accelerator.prepare(
       snake_case__	,					snake_case__	,					snake_case__	,					snake_case__	,					snake_case__     )

   # We need to keep track of how many total steps we have iterated over
   UpperCamelCase   :  Optional[int]						     =     0
   # We also need to keep track of the stating epoch so files are named properly
   UpperCamelCase   :  Union[str, Any]						     =     0

   # Now we train the model
   UpperCamelCase   :  int						     =     {}
   for epoch in range(snake_case__	,					snake_case__     ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(snake_case__     ):
            UpperCamelCase   :  Dict						     =     model(**snake_case__     )
            UpperCamelCase   :  Dict						     =     outputs.loss
            UpperCamelCase   :  Any						     =     loss / gradient_accumulation_steps
            accelerator.backward(snake_case__     )
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
      accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin     )     )     )
      accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used     )     )
      accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked     )     )
      accelerator.print(
          'Total Peak Memory consumed during the train (max): {}'.format(
              tracemalloc.peaked + bamb(tracemalloc.begin     )     )     )
      UpperCamelCase   :  Optional[int]						     =     tracemalloc.peaked + bamb(tracemalloc.begin     )
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir	,					'peak_memory_utilization.json'     )	,					'w'     ) as f:
         json.dump(snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					int:
   UpperCamelCase   :  Optional[int]						     =     argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
   parser.add_argument(
       '--model_name_or_path'	,					type=snake_case__	,					default='bert-base-cased'	,					help='Path to pretrained model or model identifier from huggingface.co/models.'	,					required=snake_case__	,					)
   parser.add_argument(
       '--output_dir'	,					type=snake_case__	,					default='.'	,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,					)
   parser.add_argument(
       '--peak_memory_upper_bound'	,					type=snake_case__	,					default=snake_case__	,					help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'	,					)
   parser.add_argument(
       '--n_train'	,					type=snake_case__	,					default=320	,					help='Number of training examples to use.'	,					)
   parser.add_argument(
       '--n_val'	,					type=snake_case__	,					default=160	,					help='Number of validation examples to use.'	,					)
   parser.add_argument(
       '--num_epochs'	,					type=snake_case__	,					default=1	,					help='Number of train epochs.'	,					)
   UpperCamelCase   :  int						     =     parser.parse_args()
   UpperCamelCase   :  Optional[Any]						     =     {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__	,					snake_case__     )


if __name__ == "__main__":
    main()








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

   # load LoRA weight from .safetensors
   UpperCamelCase   :  Union[str, Any]						     =     load_file(snake_case__     )

   UpperCamelCase   :  int						     =     []

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

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

      UpperCamelCase   :  List[Any]						     =     []
      if "lora_down" in key:
         pair_keys.append(key.replace('lora_down'	,					'lora_up'     )     )
         pair_keys.append(snake_case__     )
      else:
         pair_keys.append(snake_case__     )
         pair_keys.append(key.replace('lora_up'	,					'lora_down'     )     )

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

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

   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)



