code: 
  
  
  import argparse
  import os
  
  # New Code #
  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
  from accelerate.utils import find_executable_batch_size
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing how to combine both the gradient accumulation
  # and automatic batch size finder utilities of Accelerate to perfrom
  # automatic gradient accumulation
  #
  # 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)
  #
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # 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
  #
  ########################################################################
  
  UpperCamelCase__				       =			32
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_ = 16			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			AutoTokenizer.from_pretrained('''bert-base-cased'''			)
         _lowercase					:				Optional[int]           =			load_dataset('''glue'''    ,     '''mrpc'''			)
  
         def tokenize_function(UpperCAmelCase_			):
                # max_length=None => use the model max length (it's actually the default)
                _lowercase					:				Dict           =			tokenizer(examples['''sentence1''']    ,     examples['''sentence2''']    ,     truncation=UpperCAmelCase_    ,     max_length=UpperCAmelCase_			)
                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					:				int           =			datasets.map(
                    UpperCAmelCase_    ,     batched=UpperCAmelCase_    ,     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					:				List[str]           =			tokenized_datasets.rename_column('''label'''    ,     '''labels'''			)
  
         def collate_fn(UpperCAmelCase_			):
                # When using mixed precision we want round multiples of 8/16
                if accelerator.mixed_precision == "fp8":
                       _lowercase					:				Optional[int]           =			16
                elif accelerator.mixed_precision != "no":
                       _lowercase					:				Optional[Any]           =			8
                else:
                       _lowercase					:				Dict           =			None
  
                return tokenizer.pad(
                    UpperCAmelCase_    ,     padding='''longest'''    ,     pad_to_multiple_of=UpperCAmelCase_    ,     return_tensors='''pt'''    ,     )
  
         # Instantiate dataloaders.
         _lowercase					:				List[str]           =			DataLoader(
             tokenized_datasets['''train''']    ,     shuffle=UpperCAmelCase_    ,     collate_fn=UpperCAmelCase_    ,     batch_size=UpperCAmelCase_			)
         _lowercase					:				int           =			DataLoader(
             tokenized_datasets['''validation''']    ,     shuffle=UpperCAmelCase_    ,     collate_fn=UpperCAmelCase_    ,     batch_size=UpperCAmelCase_			)
  
         return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders
  
     UpperCamelCase__				       =			mocked_dataloaders  # noqa: F811
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,     UpperCAmelCase_			) == "1":
                _lowercase					:				int           =			2
         # Initialize accelerator
         _lowercase					:				Union[str, Any]           =			Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision			)
         # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
         _lowercase					:				Optional[int]           =			config['''lr''']
         _lowercase					:				Tuple           =			int(config['''num_epochs''']			)
         _lowercase					:				Optional[Any]           =			int(config['''seed''']			)
         _lowercase					:				Optional[Any]           =			int(config['''batch_size''']			)
  
         _lowercase					:				Optional[int]           =			evaluate.load('''glue'''    ,     '''mrpc'''			)
  
         # New Code #
         # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
         # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
         # half each time. From this, we can calculate the number of gradient accumulation steps needed
         # and modify the Accelerator object as a result
         @find_executable_batch_size(starting_batch_size=int(UpperCAmelCase_			)			)
         def inner_training_loop(UpperCAmelCase_			):
                # Since we need to modify the outside accelerator object, we need to bring it
                # to the local scope
                nonlocal accelerator
  
                # We can calculate the number of gradient accumulation steps based on the current
                # batch size vs the starting batch size
                _lowercase					:				List[Any]           =			observed_batch_size // batch_size
  
                # And then set it in the Accelerator directly:
                _lowercase					:				str           =			num_gradient_accumulation_steps
  
                # Next we need to free all of the stored model references in the Accelerator each time
                accelerator.free_memory()
  
                # And set the seed so our results are reproducable each reset
                set_seed(UpperCAmelCase_			)
  
                # Instantiate the model (we build the model here so that the seed also control new weights initialization)
                _lowercase					:				List[str]           =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,     return_dict=UpperCAmelCase_			)
  
                # 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					:				Dict           =			AdamW(params=model.parameters()    ,     lr=UpperCAmelCase_			)
                _lowercase       ,	_lowercase					:				Union[str, Any]           =			get_dataloaders(UpperCAmelCase_    ,     UpperCAmelCase_			)
  
                # Instantiate scheduler
                _lowercase					:				Dict           =			get_linear_schedule_with_warmup(
                    optimizer=UpperCAmelCase_    ,     num_warmup_steps=100    ,     num_training_steps=(len(UpperCAmelCase_			) * num_epochs)    ,     )
  
                # 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					:				Any           =			accelerator.prepare(
                    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
  
                # Now we train the model
                for epoch in range(UpperCAmelCase_			):
                       model.train()
                       for step, batch in enumerate(UpperCAmelCase_			):
                              # And perform gradient accumulation
                              with accelerator.accumulate(UpperCAmelCase_			):
                                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                                     batch.to(accelerator.device			)
                                     _lowercase					:				Tuple           =			model(**UpperCAmelCase_			)
                                     _lowercase					:				Optional[int]           =			outputs.loss
                                     accelerator.backward(UpperCAmelCase_			)
                                     optimizer.step()
                                     lr_scheduler.step()
                                     optimizer.zero_grad()
  
                       model.eval()
                       for step, batch in enumerate(UpperCAmelCase_			):
                              # We could avoid this line since we set the accelerator with `device_placement=True`.
                              batch.to(accelerator.device			)
                              with torch.no_grad():
                                     _lowercase					:				Tuple           =			model(**UpperCAmelCase_			)
                              _lowercase					:				List[str]           =			outputs.logits.argmax(dim=-1			)
                              _lowercase       ,	_lowercase					:				List[str]           =			accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
                              metric.add_batch(
                                  predictions=UpperCAmelCase_    ,     references=UpperCAmelCase_    ,     )
  
                       _lowercase					:				Optional[int]           =			metric.compute()
                       # Use accelerator.print to print only on the main process.
                       accelerator.print(F'epoch {epoch}:'    ,     UpperCAmelCase_			)
  
      # New Code #
      # And call it at the end with no arguments
      # Note: You could also refactor this outside of your training loop function
         inner_training_loop()
  
  def   UpperCamelCase__   (    )						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			argparse.ArgumentParser(description='''Simple example of training script.'''			)
         parser.add_argument(
             '''--mixed_precision'''    ,     type=UpperCAmelCase_    ,     default=UpperCAmelCase_    ,     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.'''			)
         _lowercase					:				List[Any]           =			parser.parse_args()
         # New Code #
         # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
         _lowercase					:				Tuple           =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
         training_function(UpperCAmelCase_    ,     UpperCAmelCase_			)
  
  
  if __name__ == "__main__":
     main()
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCamelCase__				       =			{
      'configuration_wav2vec2_conformer': [
          'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'Wav2Vec2ConformerConfig',
      ],
  }
  
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Wav2Vec2ConformerForAudioFrameClassification',
         'Wav2Vec2ConformerForCTC',
         'Wav2Vec2ConformerForPreTraining',
         'Wav2Vec2ConformerForSequenceClassification',
         'Wav2Vec2ConformerForXVector',
         'Wav2Vec2ConformerModel',
         'Wav2Vec2ConformerPreTrainedModel',
     ]
  
  if TYPE_CHECKING:
     from .configuration_wavaveca_conformer import (
         WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WavaVecaConformerConfig,
     )
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)