code: 
  
  import argparse
  import os
  
  import evaluate
  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
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  
  snake_case_    						=				16
  snake_case_    						=				32
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if args.with_tracking:
            lowercase__					:     Any   = Accelerator(
                cpu=args.cpu			,  mixed_precision=args.mixed_precision			,  log_with='all'			,  project_dir=args.project_dir    )
       else:
            lowercase__					:     Optional[Any]   = Accelerator(cpu=args.cpu			,  mixed_precision=args.mixed_precision    )
  
       if hasattr(args.checkpointing_steps			,  'isdigit'    ):
            if args.checkpointing_steps == "epoch":
                 lowercase__					:     Any   = args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                 lowercase__					:     List[str]   = int(args.checkpointing_steps    )
            else:
                 raise ValueError(
                     f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""    )
       else:
            lowercase__					:     Union[str, Any]   = None
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__					:     Dict   = config['lr']
       lowercase__					:     Dict   = int(config['num_epochs']    )
       lowercase__					:     int   = int(config['seed']    )
       lowercase__					:     List[Any]   = int(config['batch_size']    )
  
       # We need to initialize the trackers we use, and also store our configuration
       if args.with_tracking:
            lowercase__					:     str   = os.path.split(SCREAMING_SNAKE_CASE_    )[-1].split('.'    )[0]
            accelerator.init_trackers(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     Tuple   = AutoTokenizer.from_pretrained('bert-base-cased'    )
       lowercase__					:     Optional[Any]   = load_dataset('glue'			,  'mrpc'    )
       lowercase__					:     Any   = evaluate.load('glue'			,  'mrpc'    )
  
       def tokenize_function(SCREAMING_SNAKE_CASE_	:					List[str]    ):
            # max_length=None => use the model max length (it's actually the default)
            lowercase__					:     List[str]   = tokenizer(examples['sentence1']			,  examples['sentence2']			,  truncation=SCREAMING_SNAKE_CASE_			,  max_length=SCREAMING_SNAKE_CASE_    )
            return outputs
  
       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
            lowercase__					:     Optional[int]   = datasets.map(
                SCREAMING_SNAKE_CASE_			,  batched=SCREAMING_SNAKE_CASE_			,  remove_columns=['idx', 'sentence1', 'sentence2']			,  )
  
       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowercase__					:     int   = tokenized_datasets.rename_column('label'			,  'labels'    )
  
       # If the batch size is too big we use gradient accumulation
       lowercase__					:     Optional[Any]   = 1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowercase__					:     List[str]   = batch_size // MAX_GPU_BATCH_SIZE
            lowercase__					:     List[str]   = MAX_GPU_BATCH_SIZE
  
       def collate_fn(SCREAMING_SNAKE_CASE_	:					List[Any]    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowercase__					:     Dict   = 128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                 lowercase__					:     Any   = 16
            elif accelerator.mixed_precision != "no":
                 lowercase__					:     Union[str, Any]   = 8
            else:
                 lowercase__					:     List[Any]   = None
  
            return tokenizer.pad(
                SCREAMING_SNAKE_CASE_			,  padding='longest'			,  max_length=SCREAMING_SNAKE_CASE_			,  pad_to_multiple_of=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'			,  )
  
       # Instantiate dataloaders.
       lowercase__					:     Tuple   = DataLoader(
           tokenized_datasets['train']			,  shuffle=SCREAMING_SNAKE_CASE_			,  collate_fn=SCREAMING_SNAKE_CASE_			,  batch_size=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = DataLoader(
           tokenized_datasets['validation']			,  shuffle=SCREAMING_SNAKE_CASE_			,  collate_fn=SCREAMING_SNAKE_CASE_			,  batch_size=SCREAMING_SNAKE_CASE_    )
  
       set_seed(SCREAMING_SNAKE_CASE_    )
  
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__					:     int   = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'			,  return_dict=SCREAMING_SNAKE_CASE_    )
  
       # 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).
       lowercase__					:     Union[str, Any]   = model.to(accelerator.device    )
  
       # Instantiate optimizer
       lowercase__					:     List[str]   = AdamW(params=model.parameters()			,  lr=SCREAMING_SNAKE_CASE_    )
  
       # Instantiate scheduler
       lowercase__					:     Tuple   = get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE_			,  num_warmup_steps=100			,  num_training_steps=(len(SCREAMING_SNAKE_CASE_    ) * num_epochs) // gradient_accumulation_steps			,  )
  
       # 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.
       lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     Optional[Any]   = accelerator.prepare(
           SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       # We need to keep track of how many total steps we have iterated over
       lowercase__					:     Dict   = 0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__					:     Any   = 0
  
       # Potentially 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    )
                 lowercase__					:     Union[str, Any]   = os.path.basename(args.resume_from_checkpoint    )
            else:
                 # Get the most recent checkpoint
                 lowercase__					:     Optional[Any]   = [f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
                 dirs.sort(key=os.path.getctime    )
                 lowercase__					:     Optional[Any]   = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowercase__					:     List[Any]   = os.path.splitext(SCREAMING_SNAKE_CASE_    )[0]
  
            if "epoch" in training_difference:
                 lowercase__					:     Dict   = int(training_difference.replace('epoch_'			,  ''    )    ) + 1
                 lowercase__					:     Union[str, Any]   = None
            else:
                 lowercase__					:     Optional[int]   = int(training_difference.replace('step_'			,  ''    )    )
                 lowercase__					:     List[str]   = resume_step // len(SCREAMING_SNAKE_CASE_    )
                 resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_    )
  
      # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            model.train()
            if args.with_tracking:
                 lowercase__					:     Tuple   = 0
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                 # We need to skip steps until we reach the resumed step
                 lowercase__					:     str   = accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 overall_step += resume_step
            else:
                 # After the first iteration though, we need to go back to the original dataloader
                 lowercase__					:     int   = train_dataloader
            for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 batch.to(accelerator.device    )
                 lowercase__					:     Union[str, Any]   = model(**SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     Dict   = outputs.loss
                 lowercase__					:     Union[str, Any]   = loss / gradient_accumulation_steps
                 # We keep track of the loss at each epoch
                 if args.with_tracking:
                      total_loss += loss.detach().float()
                 accelerator.backward(SCREAMING_SNAKE_CASE_    )
                 if step % gradient_accumulation_steps == 0:
                      optimizer.step()
                      lr_scheduler.step()
                      optimizer.zero_grad()
  
                 overall_step += 1
  
                 if isinstance(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
                      lowercase__					:     List[Any]   = f"""step_{overall_step}"""
                      if overall_step % checkpointing_steps == 0:
                           if args.output_dir is not None:
                                lowercase__					:     Tuple   = os.path.join(args.output_dir			,  SCREAMING_SNAKE_CASE_    )
                           accelerator.save_state(SCREAMING_SNAKE_CASE_    )
  
            model.eval()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 batch.to(accelerator.device    )
                 with torch.no_grad():
                      lowercase__					:     int   = model(**SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     Any   = outputs.logits.argmax(dim=-1    )
                 lowercase__   ,				lowercase__					:     int   = accelerator.gather_for_metrics((predictions, batch['labels'])    )
                 metric.add_batch(
                     predictions=SCREAMING_SNAKE_CASE_			,  references=SCREAMING_SNAKE_CASE_			,  )
  
            lowercase__					:     Union[str, Any]   = metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""			,  SCREAMING_SNAKE_CASE_    )
            if args.with_tracking:
                 accelerator.log(
                     {
                         'accuracy': eval_metric['accuracy'],
                         'f1': eval_metric['f1'],
                         'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE_    ),
                         'epoch': epoch,
                     }			,  step=SCREAMING_SNAKE_CASE_			,  )
  
            if checkpointing_steps == "epoch":
                 lowercase__					:     str   = f"""epoch_{epoch}"""
                 if args.output_dir is not None:
                      lowercase__					:     Any   = os.path.join(args.output_dir			,  SCREAMING_SNAKE_CASE_    )
                 accelerator.save_state(SCREAMING_SNAKE_CASE_    )
  
       if args.with_tracking:
            accelerator.end_training()
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict   = argparse.ArgumentParser(description='Simple example of training script.'    )
       parser.add_argument(
           '--mixed_precision'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  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('--cpu'			,  action='store_true'			,  help='If passed, will train on the CPU.'    )
       parser.add_argument(
           '--checkpointing_steps'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			,  )
       parser.add_argument(
           '--resume_from_checkpoint'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='If the training should continue from a checkpoint folder.'			,  )
       parser.add_argument(
           '--with_tracking'			,  action='store_true'			,  help='Whether to load in all available experiment trackers from the environment and use them for logging.'			,  )
       parser.add_argument(
           '--output_dir'			,  type=SCREAMING_SNAKE_CASE_			,  default='.'			,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,  )
       parser.add_argument(
           '--project_dir'			,  type=SCREAMING_SNAKE_CASE_			,  default='logs'			,  help='Location on where to store experiment tracking logs` and relevent project information'			,  )
       lowercase__					:     Union[str, Any]   = parser.parse_args()
       lowercase__					:     Dict   = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
  
  if __name__ == "__main__":
         main()
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  