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
  #
  ########################################################################
  
  
  a     :			Any					    =						1_6
  a     :			List[Any]					    =						3_2
  def 		snake_case__       (		lowercase       ,							lowercase				):
     # Initialize accelerator
     if args.with_tracking:
        lowerCAmelCase_:       Union[str, Any]         =  Accelerator(
            cpu=args.cpu       ,							mixed_precision=args.mixed_precision       ,							log_with="all"       ,							project_dir=args.project_dir				)
     else:
        lowerCAmelCase_:       Any         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)
  
     if hasattr(args.checkpointing_steps       ,							"isdigit"				):
        if args.checkpointing_steps == "epoch":
           lowerCAmelCase_:       int         =  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
           lowerCAmelCase_:       Optional[int]         =  int(args.checkpointing_steps				)
        else:
           raise ValueError(
               F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
     else:
        lowerCAmelCase_:       Tuple         =  None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_:       List[Any]         =  config["lr"]
     lowerCAmelCase_:       List[str]         =  int(config["num_epochs"]				)
     lowerCAmelCase_:       Tuple         =  int(config["seed"]				)
     lowerCAmelCase_:       Optional[Any]         =  int(config["batch_size"]				)
  
     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
        lowerCAmelCase_:       Union[str, Any]         =  os.path.split(lowercase__				)[-1].split("."				)[0]
        accelerator.init_trackers(lowercase__       ,							lowercase__				)
  
     lowerCAmelCase_:       Optional[int]         =  AutoTokenizer.from_pretrained("bert-base-cased"				)
     lowerCAmelCase_:       int         =  load_dataset("glue"       ,							"mrpc"				)
     lowerCAmelCase_:       Optional[Any]         =  evaluate.load("glue"       ,							"mrpc"				)
  
     def tokenize_function(lowercase				):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase_:       Optional[int]         =  tokenizer(examples["sentence1"]       ,							examples["sentence2"]       ,							truncation=lowercase__       ,							max_length=lowercase__				)
        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():
        lowerCAmelCase_:       Dict         =  datasets.map(
            lowercase__       ,							batched=lowercase__       ,							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
     lowerCAmelCase_:       str         =  tokenized_datasets.rename_column("label"       ,							"labels"				)
  
     # If the batch size is too big we use gradient accumulation
     lowerCAmelCase_:       str         =  1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        lowerCAmelCase_:       Tuple         =  batch_size // MAX_GPU_BATCH_SIZE
        lowerCAmelCase_:       List[Any]         =  MAX_GPU_BATCH_SIZE
  
     def collate_fn(lowercase				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase_:       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":
           lowerCAmelCase_:       Any         =  16
        elif accelerator.mixed_precision != "no":
           lowerCAmelCase_:       List[str]         =  8
        else:
           lowerCAmelCase_:       Tuple         =  None
  
        return tokenizer.pad(
            lowercase__       ,							padding="longest"       ,							max_length=lowercase__       ,							pad_to_multiple_of=lowercase__       ,							return_tensors="pt"       ,							)
  
     # Instantiate dataloaders.
     lowerCAmelCase_:       Union[str, Any]         =  DataLoader(
         tokenized_datasets["train"]       ,							shuffle=lowercase__       ,							collate_fn=lowercase__       ,							batch_size=lowercase__				)
     lowerCAmelCase_:       Dict         =  DataLoader(
         tokenized_datasets["validation"]       ,							shuffle=lowercase__       ,							collate_fn=lowercase__       ,							batch_size=lowercase__				)
  
     set_seed(lowercase__				)
  
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase_:       List[str]         =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,							return_dict=lowercase__				)
  
     # 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).
     lowerCAmelCase_:       str         =  model.to(accelerator.device				)
  
     # Instantiate optimizer
     lowerCAmelCase_:       Tuple         =  AdamW(params=model.parameters()       ,							lr=lowercase__				)
  
     # Instantiate scheduler
     lowerCAmelCase_:       str         =  get_linear_schedule_with_warmup(
         optimizer=lowercase__       ,							num_warmup_steps=100       ,							num_training_steps=(len(lowercase__				) * 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.
     lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Any         =  accelerator.prepare(
         lowercase__       ,							lowercase__       ,							lowercase__       ,							lowercase__       ,							lowercase__				)
  
     # We need to keep track of how many total steps we have iterated over
     lowerCAmelCase_:       Tuple         =  0
     # We also need to keep track of the stating epoch so files are named properly
     lowerCAmelCase_:       Tuple         =  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				)
           lowerCAmelCase_:       Optional[int]         =  os.path.basename(args.resume_from_checkpoint				)
        else:
           # Get the most recent checkpoint
           lowerCAmelCase_:       Optional[Any]         =  [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
           dirs.sort(key=os.path.getctime				)
           lowerCAmelCase_:       Any         =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        lowerCAmelCase_:       Dict         =  os.path.splitext(lowercase__				)[0]
  
        if "epoch" in training_difference:
           lowerCAmelCase_:       Dict         =  int(training_difference.replace("epoch_"       ,							""				)				) + 1
           lowerCAmelCase_:       Optional[Any]         =  None
        else:
           lowerCAmelCase_:       Any         =  int(training_difference.replace("step_"       ,							""				)				)
           lowerCAmelCase_:       Tuple         =  resume_step // len(lowercase__				)
           resume_step -= starting_epoch * len(lowercase__				)
  
      # Now we train the model
     for epoch in range(lowercase__       ,							lowercase__				):
        model.train()
        if args.with_tracking:
           lowerCAmelCase_:       Union[str, Any]         =  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
           lowerCAmelCase_:       str         =  accelerator.skip_first_batches(lowercase__       ,							lowercase__				)
           overall_step += resume_step
        else:
           # After the first iteration though, we need to go back to the original dataloader
           lowerCAmelCase_:       Dict         =  train_dataloader
        for step, batch in enumerate(lowercase__				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           batch.to(accelerator.device				)
           lowerCAmelCase_:       Optional[Any]         =  model(**lowercase__				)
           lowerCAmelCase_:       Any         =  outputs.loss
           lowerCAmelCase_:       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(lowercase__				)
           if step % gradient_accumulation_steps == 0:
              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
  
           overall_step += 1
  
           if isinstance(lowercase__       ,							lowercase__				):
              lowerCAmelCase_:       Dict         =  F'''step_{overall_step}'''
              if overall_step % checkpointing_steps == 0:
                 if args.output_dir is not None:
                    lowerCAmelCase_:       Optional[int]         =  os.path.join(args.output_dir       ,							lowercase__				)
                 accelerator.save_state(lowercase__				)
  
        model.eval()
        for step, batch in enumerate(lowercase__				):
           # We could avoid this line since we set the accelerator with `device_placement=True`.
           batch.to(accelerator.device				)
           with torch.no_grad():
              lowerCAmelCase_:       int         =  model(**lowercase__				)
           lowerCAmelCase_:       List[str]         =  outputs.logits.argmax(dim=-1				)
           lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
           metric.add_batch(
               predictions=lowercase__       ,							references=lowercase__       ,							)
  
        lowerCAmelCase_:       int         =  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''       ,							lowercase__				)
        if args.with_tracking:
           accelerator.log(
               {
                   "accuracy": eval_metric["accuracy"],
                   "f1": eval_metric["f1"],
                   "train_loss": total_loss.item() / len(lowercase__				),
                   "epoch": epoch,
               }       ,							step=lowercase__       ,							)
  
        if checkpointing_steps == "epoch":
           lowerCAmelCase_:       Tuple         =  F'''epoch_{epoch}'''
           if args.output_dir is not None:
              lowerCAmelCase_:       Optional[int]         =  os.path.join(args.output_dir       ,							lowercase__				)
           accelerator.save_state(lowercase__				)
  
     if args.with_tracking:
        accelerator.end_training()
  def 		snake_case__       (		):
     lowerCAmelCase_:       Optional[Any]         =  argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument(
         "--mixed_precision"       ,							type=lowercase__       ,							default=lowercase__       ,							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=lowercase__       ,							default=lowercase__       ,							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=lowercase__       ,							default=lowercase__       ,							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=lowercase__       ,							default="."       ,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,							)
     parser.add_argument(
         "--project_dir"       ,							type=lowercase__       ,							default="logs"       ,							help="Location on where to store experiment tracking logs` and relevent project information"       ,							)
     lowerCAmelCase_:       Dict         =  parser.parse_args()
     lowerCAmelCase_:       Optional[Any]         =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowercase__       ,							lowercase__				)
  
  
  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
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)