code: 
  """simple docstring"""
  
  
  
  
  
  
  
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  SCREAMING_SNAKE_CASE_     				=   '\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model\'s predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n'
  
  SCREAMING_SNAKE_CASE_     				=   '\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:\n    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `\'ovr\'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == \'macro\'`, because class imbalance affects the composition of each of the \'rest\' groupings.\n    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric("roc_auc")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results[\'roc_auc\']])\n        [0.83, 0.38, 0.94]\n'
  
  SCREAMING_SNAKE_CASE_     				=   '\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n'
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,	_KWARGS_DESCRIPTION			)
  class     __a			(					datasets.Metric			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return datasets.MetricInfo(
                description=_DESCRIPTION							,citation=_CITATION							,inputs_description=_KWARGS_DESCRIPTION							,features=datasets.Features(
                    {
                        "prediction_scores": datasets.Sequence(datasets.Value("float"		)		),
                        "references": datasets.Value("int32"		),
                    }
                    if self.config_name == "multiclass"
                    else {
                        "references": datasets.Sequence(datasets.Value("int32"		)		),
                        "prediction_scores": datasets.Sequence(datasets.Value("float"		)		),
                    }
                    if self.config_name == "multilabel"
                    else {
                        "references": datasets.Value("int32"		),
                        "prediction_scores": datasets.Value("float"		),
                    }		)							,reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"]							,)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_="macro"							,lowerCamelCase_=None							,lowerCamelCase_=None							,lowerCamelCase_="raise"							,lowerCamelCase_=None							,):
  
  
  
            '''simple docstring'''
  
            return {
                "roc_auc": roc_auc_score(
                    lowerCamelCase_							,lowerCamelCase_							,average=lowerCamelCase_							,sample_weight=lowerCamelCase_							,max_fpr=lowerCamelCase_							,multi_class=lowerCamelCase_							,labels=lowerCamelCase_							,)
            }
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False							,lowerCamelCase_=1024							,lowerCamelCase_=1024							,lowerCamelCase_=3.6							,lowerCamelCase_=False							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					tokenizer
            _UpperCAmelCase   	=					tokenizer.bos_token_id
            _UpperCAmelCase   	=					dataset
            _UpperCAmelCase   	=					seq_length
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					infinite
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					tokenized
  
            if self.tokenized:
               _UpperCAmelCase   	=					seq_length * num_of_sequences
               _UpperCAmelCase   	=					"input_ids"
            else:
               _UpperCAmelCase   	=					seq_length * chars_per_token * num_of_sequences
               _UpperCAmelCase   	=					"content"
  
  
  
  
  
  
  
         def __iter__(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					iter(self.dataset		)
            _UpperCAmelCase   	=					True
            while more_examples:
               _UpperCAmelCase	,							_UpperCAmelCase   	=					[], 0
               while True:
                  if buffer_len >= self.max_buffer_size:
                     break
                  try:
                     buffer.append(next(lowerCamelCase_		)[self.content_field]		)
                     buffer_len += len(buffer[-1]		)
                  except StopIteration:
                     if self.infinite:
                        _UpperCAmelCase   	=					iter(self.dataset		)
                        self.epoch += 1
                        logger.info(f'''Dataset epoch: {self.epoch}'''		)
                     else:
                        _UpperCAmelCase   	=					False
                        break
               if self.tokenized:
                  _UpperCAmelCase   	=					buffer
               else:
                  _UpperCAmelCase   	=					self.tokenizer(lowerCamelCase_							,truncation=lowerCamelCase_		)["input_ids"]
               _UpperCAmelCase   	=					[]
               for tokenized_input in tokenized_inputs:
                  all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
               for i in range(0							,len(lowerCamelCase_		)							,self.seq_length		):
                  _UpperCAmelCase   	=					all_token_ids[i : i + self.seq_length]
                  if len(lowerCamelCase_		) == self.seq_length:
                     self.current_size += 1
                     yield torch.tensor(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=1000		):
  
  
  
            '''simple docstring'''
  
            return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)
  
  
  
  def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
     _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
     _UpperCAmelCase   	=					logging.getLogger(__name__				)
     _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
     log_dir.mkdir(exist_ok=a_				)
     _UpperCAmelCase   	=					F'''debug_{accelerator.process_index}.log'''
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO    ,  handlers=[logging.FileHandler(log_dir / filename				), logging.StreamHandler()]    ,  )
     if accelerator.is_main_process:  # we only want to setup logging once
        accelerator.init_trackers(a_    ,  vars(a_				)				)
        _UpperCAmelCase   	=					accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO				)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
     else:
        _UpperCAmelCase   	=					""
        logger.setLevel(logging.ERROR				)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
     return logger, run_name
  def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
     _UpperCAmelCase   	=					{"streaming": True}
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
     return train_dataloader, eval_dataloader
  def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
     _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
     for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay				):
           params_without_wd.append(a_				)
        else:
           params_with_wd.append(a_				)
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[str]				)     ->       Optional[Any]:
     logger.info(F'''Step {step}: {metrics}'''				)
     if accelerator.is_main_process:
        accelerator.log(a_    ,  a_				)
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
     # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
     _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).config
     _UpperCAmelCase   	=					4 if args.gradient_checkpointing else 3
     _UpperCAmelCase   	=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     _UpperCAmelCase   	=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     _UpperCAmelCase   	=					factor * (
         1.0
         + (args.seq_length / (6.0 * config_model.n_embd))
         + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
     )
     _UpperCAmelCase   	=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops
  def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     model.eval()
     _UpperCAmelCase   	=					[]
     for step, batch in enumerate(a_				):
        with torch.no_grad():
           _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
        _UpperCAmelCase   	=					outputs.loss.repeat(args.valid_batch_size				)
        losses.append(accelerator.gather(a_				)				)
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
           break
     _UpperCAmelCase   	=					torch.cat(a_				)
     _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
     try:
        _UpperCAmelCase   	=					torch.exp(a_				)
     except OverflowError:
        _UpperCAmelCase   	=					float("inf"				)
     return loss.item(), perplexity.item()
  
  
  # Settings
  SCREAMING_SNAKE_CASE_     				=   HfArgumentParser(TrainingArguments)
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
  # Accelerator
  SCREAMING_SNAKE_CASE_     				=   ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
  SCREAMING_SNAKE_CASE_     				=   Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
  SCREAMING_SNAKE_CASE_     				=   {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  SCREAMING_SNAKE_CASE_     				=   Namespace(**vars(args), **acc_state)
  SCREAMING_SNAKE_CASE_     				=   accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
    SCREAMING_SNAKE_CASE_     				=   Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
    hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  SCREAMING_SNAKE_CASE_     				=   AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
  SCREAMING_SNAKE_CASE_     				=   AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  SCREAMING_SNAKE_CASE_     				=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  SCREAMING_SNAKE_CASE_     				=   get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  def 			__UpperCAmelCase		(		)     ->       int:
     return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
      accelerator.load_state(args.resume_from_checkpoint)
      SCREAMING_SNAKE_CASE_     				=   os.path.basename(args.resume_from_checkpoint)
    else:
      # Get the most recent checkpoint
      SCREAMING_SNAKE_CASE_     				=   [f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
      dirs.sort(key=os.path.getctime)
      SCREAMING_SNAKE_CASE_     				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    SCREAMING_SNAKE_CASE_     				=   os.path.splitext(path)[0]
    SCREAMING_SNAKE_CASE_     				=   int(training_difference.replace('step_', ''))
  
  # Train model
  model.train()
  SCREAMING_SNAKE_CASE_     				=   0
  SCREAMING_SNAKE_CASE_     				=   time.time()
  SCREAMING_SNAKE_CASE_     				=   0
  for step, batch in enumerate(train_dataloader, start=1):
    if args.resume_from_checkpoint and step < resume_step:
      continue  # we need to skip steps until we reach the resumed step
    SCREAMING_SNAKE_CASE_     				=   model(batch, labels=batch, use_cache=False).loss
    SCREAMING_SNAKE_CASE_     				=   accelerator.gather(loss.repeat(args.train_batch_size)).mean()
    loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
    log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
    SCREAMING_SNAKE_CASE_     				=   loss / args.gradient_accumulation_steps
    if step % args.gradient_accumulation_steps != 0:
      # Prevent backward from doing gradient all_reduce in every step
      if accelerator.distributed_type == DistributedType.MULTI_GPU:
        with model.no_sync():
          accelerator.backward(loss)
      else:
        accelerator.backward(loss)
    else:
      SCREAMING_SNAKE_CASE_     				=   get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      SCREAMING_SNAKE_CASE_     				=   time.time() - t_start
      SCREAMING_SNAKE_CASE_     				=   compute_tflops(elapsed_time, accelerator, args)
      log_metrics(
          step,
          {
              'steps': completed_steps,
              'loss/train': loss_tracking,
              'lr': lr,
              'tflops': tflops,
              'time_per_iteration': elapsed_time,
          },
      )
      SCREAMING_SNAKE_CASE_     				=   time.time()
      SCREAMING_SNAKE_CASE_     				=   0
      completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
      logger.info('Evaluating and saving model checkpoint')
      SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
      log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
      accelerator.wait_for_everyone()
      SCREAMING_SNAKE_CASE_     				=   os.path.join(args.save_dir, f'''step_{step}''')
      accelerator.save_state(save_dir)
      if accelerator.is_main_process:
        hf_repo.push_to_hub(commit_message=f'''step {step}''')
      model.train()
    if completed_steps >= args.max_train_steps:
      break
  
  # Evaluate and save the last checkpoint
  logger.info('Evaluating and saving model after training')
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  SCREAMING_SNAKE_CASE_     				=   accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  SCREAMING_SNAKE_CASE_     				=   os.path.join(args.save_dir, f'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
    hf_repo.push_to_hub(commit_message='final model')
  
  