code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import os
  import re
  
  import numpy as np
  import PIL
  import torch
  from timm import create_model
  from torch.optim.lr_scheduler import OneCycleLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
  
  from accelerate import Accelerator
  def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Optional[Any]:
     _UpperCAmelCase   	=					fname.split(os.path.sep				)[-1]
     return re.search(r"^(.*)_\d+\.jpg$"    ,  a_				).groups()[0]
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=None							,lowerCamelCase_=None		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					file_names
            _UpperCAmelCase   	=					image_transform
            _UpperCAmelCase   	=					label_to_id
  
  
  
  
  
  
  
         def __len__(					self		):
  
  
  
            '''simple docstring'''
  
            return len(self.file_names		)
  
  
  
  
  
  
  
         def __getitem__(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.file_names[idx]
            _UpperCAmelCase   	=					PIL.Image.open(lowerCamelCase_		)
            _UpperCAmelCase   	=					raw_image.convert("RGB"		)
            if self.image_transform is not None:
               _UpperCAmelCase   	=					self.image_transform(lowerCamelCase_		)
            _UpperCAmelCase   	=					extract_label(lowerCamelCase_		)
            if self.label_to_id is not None:
               _UpperCAmelCase   	=					self.label_to_id[label]
            return {"image": image, "label": label}
  
  
  
  def 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		Union[str, Any]				)     ->       Dict:
     # Initialize accelerator
     _UpperCAmelCase   	=					Accelerator(cpu=args.cpu    ,  mixed_precision=args.mixed_precision				)
  
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCAmelCase   	=					config["lr"]
     _UpperCAmelCase   	=					int(config["num_epochs"]				)
     _UpperCAmelCase   	=					int(config["seed"]				)
     _UpperCAmelCase   	=					int(config["batch_size"]				)
     _UpperCAmelCase   	=					config["image_size"]
     if not isinstance(a_    ,  (list, tuple)				):
        _UpperCAmelCase   	=					(image_size, image_size)
  
     # Grab all the image filenames
     _UpperCAmelCase   	=					[os.path.join(args.data_dir    ,  a_				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]
  
     # Build the label correspondences
     _UpperCAmelCase   	=					[extract_label(a_				) for fname in file_names]
     _UpperCAmelCase   	=					list(set(a_				)				)
     id_to_label.sort()
     _UpperCAmelCase   	=					{lbl: i for i, lbl in enumerate(a_				)}
  
     # Set the seed before splitting the data.
     np.random.seed(a_				)
     torch.manual_seed(a_				)
     torch.cuda.manual_seed_all(a_				)
  
     # Split our filenames between train and validation
     _UpperCAmelCase   	=					np.random.permutation(len(a_				)				)
     _UpperCAmelCase   	=					int(0.8 * len(a_				)				)
     _UpperCAmelCase   	=					random_perm[:cut]
     _UpperCAmelCase   	=					random_perm[cut:]
  
     # For training we use a simple RandomResizedCrop
     _UpperCAmelCase   	=					Compose([RandomResizedCrop(a_    ,  scale=(0.5, 1.0)				), ToTensor()]				)
     _UpperCAmelCase   	=					PetsDataset(
         [file_names[i] for i in train_split]    ,  image_transform=a_    ,  label_to_id=a_				)
  
     # For evaluation, we use a deterministic Resize
     _UpperCAmelCase   	=					Compose([Resize(a_				), ToTensor()]				)
     _UpperCAmelCase   	=					PetsDataset([file_names[i] for i in eval_split]    ,  image_transform=a_    ,  label_to_id=a_				)
  
     # Instantiate dataloaders.
     _UpperCAmelCase   	=					DataLoader(a_    ,  shuffle=a_    ,  batch_size=a_    ,  num_workers=4				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  shuffle=a_    ,  batch_size=a_    ,  num_workers=4				)
  
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _UpperCAmelCase   	=					create_model("resnet50d"    ,  pretrained=a_    ,  num_classes=len(a_				)				)
  
     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     _UpperCAmelCase   	=					model.to(accelerator.device				)
  
     # Freezing the base model
     for param in model.parameters():
        _UpperCAmelCase   	=					False
     for param in model.get_classifier().parameters():
        _UpperCAmelCase   	=					True
  
     # We normalize the batches of images to be a bit faster.
     _UpperCAmelCase   	=					torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
     _UpperCAmelCase   	=					torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)
  
     # Instantiate optimizer
     _UpperCAmelCase   	=					torch.optim.Adam(params=model.parameters()    ,  lr=lr / 25				)
  
     # Instantiate learning rate scheduler
     _UpperCAmelCase   	=					OneCycleLR(optimizer=a_    ,  max_lr=a_    ,  epochs=a_    ,  steps_per_epoch=len(a_				)				)
  
     # 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   	=					accelerator.prepare(
         a_    ,  a_    ,  a_    ,  a_    ,  a_				)
  
     # Now we train the model
     for epoch in range(a_				):
        model.train()
        for step, batch in enumerate(a_				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           _UpperCAmelCase   	=					{k: v.to(accelerator.device				) for k, v in batch.items()}
           _UpperCAmelCase   	=					(batch["image"] - mean) / std
           _UpperCAmelCase   	=					model(a_				)
           _UpperCAmelCase   	=					torch.nn.functional.cross_entropy(a_    ,  batch["label"]				)
           accelerator.backward(a_				)
           optimizer.step()
           lr_scheduler.step()
           optimizer.zero_grad()
  
        model.eval()
        _UpperCAmelCase   	=					0
        _UpperCAmelCase   	=					0
        for _, batch in enumerate(a_				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           _UpperCAmelCase   	=					{k: v.to(accelerator.device				) for k, v in batch.items()}
           _UpperCAmelCase   	=					(batch["image"] - mean) / std
           with torch.no_grad():
              _UpperCAmelCase   	=					model(a_				)
           _UpperCAmelCase   	=					outputs.argmax(dim=-1				)
           _UpperCAmelCase	,							_UpperCAmelCase   	=					accelerator.gather_for_metrics((predictions, batch["label"])				)
           _UpperCAmelCase   	=					predictions == references
           num_elems += accurate_preds.shape[0]
           accurate += accurate_preds.long().sum()
  
        _UpperCAmelCase   	=					accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}'''				)
  def 			__UpperCAmelCase		(		)     ->       Any:
     _UpperCAmelCase   	=					argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument("--data_dir"    ,  required=a_    ,  help="The data folder on disk."				)
     parser.add_argument(
         "--mixed_precision"    ,  type=a_    ,  default=a_    ,  choices=["no", "fp16", "bf16", "fp8"]    ,  help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU."    ,  )
     parser.add_argument(
         "--checkpointing_steps"    ,  type=a_    ,  default=a_    ,  help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,  )
     parser.add_argument("--cpu"    ,  action="store_true"    ,  help="If passed, will train on the CPU."				)
     _UpperCAmelCase   	=					parser.parse_args()
     _UpperCAmelCase   	=					{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
     training_function(a_    ,  a_				)
  
  
  if __name__ == "__main__":
    main()
  
  
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')
  
  