code: 
  
  import argparse
  import gc
  import os
  import threading
  
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
      set_seed,
  )
  
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  
  
  ########################################################################
  # 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)
  #   - FSDP
  #
  # 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   :     List[Any]      =			16
  __A   :     int      =			32
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> List[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      return int(x / 2**20				)
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __enter__(	self			:    Any    ):
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          lowerCAmelCase				:			Optional[Any]    			=      torch.cuda.memory_allocated()
          lowerCAmelCase				:			int    			=      psutil.Process()
  
          lowerCAmelCase				:			List[str]    			=      self.cpu_mem_used()
          lowerCAmelCase				:			Dict    			=      True
          lowerCAmelCase				:			Optional[Any]    			=      threading.Thread(target=self.peak_monitor_func    )
          lowerCAmelCase				:			Dict    			=      True
          peak_monitor_thread.start()
          return self
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          return self.process.memory_info().rss
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			Union[str, Any]    			=      -1
  
          while True:
              lowerCAmelCase				:			str    			=      max(self.cpu_mem_used()    ,				self.cpu_peak    )
  
              # can't sleep or will not catch the peak right (this comment is here on purpose)
              # time.sleep(0.001) # 1msec
  
              if not self.peak_monitoring:
                  break
  
  
  
  
  
  
      def __exit__(	self			:    int    ,				*UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			Tuple    			=      False
  
          gc.collect()
          torch.cuda.empty_cache()
          lowerCAmelCase				:			Union[str, Any]    			=      torch.cuda.memory_allocated()
          lowerCAmelCase				:			List[Any]    			=      torch.cuda.max_memory_allocated()
          lowerCAmelCase				:			Tuple    			=      bamb(self.end - self.begin    )
          lowerCAmelCase				:			Optional[int]    			=      bamb(self.peak - self.begin    )
  
          lowerCAmelCase				:			List[Any]    			=      self.cpu_mem_used()
          lowerCAmelCase				:			Dict    			=      bamb(self.cpu_end - self.cpu_begin    )
          lowerCAmelCase				:			Optional[Any]    			=      bamb(self.cpu_peak - self.cpu_begin    )
          # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
  
  
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders
  
   __A   :     int      =			mocked_dataloaders  # noqa: F811
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> List[Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if os.environ.get('TESTING_MOCKED_DATALOADERS',      _UpperCAmelCase				) == "1":
          lowerCAmelCase				:			Optional[int]    			=      2
  
      # New Code #
      # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
      lowerCAmelCase				:			List[str]    			=      FullyShardedDataParallelPlugin(
          state_dict_config=FullStateDictConfig(offload_to_cpu=_UpperCAmelCase,      ranka_only=_UpperCAmelCase				),      optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_UpperCAmelCase,      ranka_only=_UpperCAmelCase				),      )
  
      # Initialize accelerator
      if args.with_tracking:
          lowerCAmelCase				:			Any    			=      Accelerator(
              cpu=args.cpu,      mixed_precision=args.mixed_precision,      log_with='wandb',      project_dir=args.logging_dir,      fsdp_plugin=_UpperCAmelCase,      )
      else:
          lowerCAmelCase				:			Optional[int]    			=      Accelerator(fsdp_plugin=_UpperCAmelCase				)
      accelerator.print(accelerator.distributed_type				)
  
      if hasattr(args.checkpointing_steps,      'isdigit'				):
          if args.checkpointing_steps == "epoch":
              lowerCAmelCase				:			List[Any]    			=      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				:			List[Any]    			=      None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase				:			Union[str, Any]    			=      config['lr']
      lowerCAmelCase				:			Union[str, Any]    			=      int(config['num_epochs']				)
      lowerCAmelCase				:			Tuple    			=      int(config['seed']				)
      lowerCAmelCase				:			Any    			=      int(config['batch_size']				)
  
      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
          lowerCAmelCase				:			Dict    			=      vars(_UpperCAmelCase				)
          accelerator.init_trackers('fsdp_glue_no_trainer',      _UpperCAmelCase				)
  
      lowerCAmelCase				:			List[str]    			=      AutoTokenizer.from_pretrained(args.model_name_or_path				)
      lowerCAmelCase				:			Any    			=      load_dataset('glue',      'mrpc'				)
      lowerCAmelCase				:			Any    			=      evaluate.load('glue',      'mrpc'				)
  
      def tokenize_function(_UpperCAmelCase				):
          # max_length=None => use the model max length (it's actually the default)
          lowerCAmelCase				:			int    			=      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():
          lowerCAmelCase				:			Union[str, Any]    			=      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
      lowerCAmelCase				:			Union[str, Any]    			=      tokenized_datasets.rename_column('label',      'labels'				)
  
      # If the batch size is too big we use gradient accumulation
      lowerCAmelCase				:			Optional[Any]    			=      1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          lowerCAmelCase				:			str    			=      batch_size // MAX_GPU_BATCH_SIZE
          lowerCAmelCase				:			Optional[int]    			=      MAX_GPU_BATCH_SIZE
  
      def collate_fn(_UpperCAmelCase				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowerCAmelCase				:			List[str]    			=      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				:			Tuple    			=      16
          elif accelerator.mixed_precision != "no":
              lowerCAmelCase				:			List[Any]    			=      8
          else:
              lowerCAmelCase				:			Union[str, Any]    			=      None
  
          return tokenizer.pad(
              _UpperCAmelCase,      padding='longest',      max_length=_UpperCAmelCase,      pad_to_multiple_of=_UpperCAmelCase,      return_tensors='pt',      )
  
      # Instantiate dataloaders.
      lowerCAmelCase				:			int    			=      DataLoader(
          tokenized_datasets['train'],      shuffle=_UpperCAmelCase,      collate_fn=_UpperCAmelCase,      batch_size=_UpperCAmelCase				)
      lowerCAmelCase				:			int    			=      DataLoader(
          tokenized_datasets['validation'],      shuffle=_UpperCAmelCase,      collate_fn=_UpperCAmelCase,      batch_size=_UpperCAmelCase				)
  
      set_seed(_UpperCAmelCase				)
  
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase				:			Optional[int]    			=      AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path,      return_dict=_UpperCAmelCase,      low_cpu_mem_usage=_UpperCAmelCase				)
  
      # New Code #
      # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
      lowerCAmelCase				:			int    			=      accelerator.prepare(_UpperCAmelCase				)
      accelerator.print(_UpperCAmelCase				)
  
      # Instantiate optimizer
      # New Code #
      # For FSDP feature, at present it doesn't support multiple parameter groups,
      # so we need to create a single parameter group for the whole model
      lowerCAmelCase				:			str    			=      torch.optim.AdamW(params=model.parameters(),      lr=_UpperCAmelCase,      weight_decay=2e-4				)
  
      # Instantiate scheduler
      lowerCAmelCase				:			Optional[Any]    			=      get_linear_schedule_with_warmup(
          optimizer=_UpperCAmelCase,      num_warmup_steps=10,      num_training_steps=(len(_UpperCAmelCase				) * num_epochs) // gradient_accumulation_steps,      )
  
      # New Code #
      # For FSDP feature, prepare everything except the model as we have already prepared the model
      # before creating the optimizer
      # 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				:			Any    			=      accelerator.prepare(
          _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)
  
      lowerCAmelCase				:			Optional[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				)
              lowerCAmelCase				:			Optional[Any]    			=      os.path.basename(args.resume_from_checkpoint				)
          else:
              # Get the most recent checkpoint
              lowerCAmelCase				:			Tuple    			=      [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
              dirs.sort(key=os.path.getctime				)
              lowerCAmelCase				:			Optional[int]    			=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          lowerCAmelCase				:			str    			=      os.path.splitext(_UpperCAmelCase				)[0]
  
          if "epoch" in training_difference:
              num_epochs -= int(training_difference.replace('epoch_',      ''				)				)
              lowerCAmelCase				:			str    			=      None
          else:
              lowerCAmelCase				:			Optional[int]    			=      int(training_difference.replace('step_',      ''				)				)
              num_epochs -= resume_step // len(_UpperCAmelCase				)
              # If resuming by step, we also need to know exactly how far into the DataLoader we went
              lowerCAmelCase				:			str    			=      (num_epochs * len(_UpperCAmelCase				)) - resume_step
  
      # Now we train the model
      for epoch in range(_UpperCAmelCase				):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
              model.train()
              if args.with_tracking:
                  lowerCAmelCase				:			Optional[Any]    			=      0
              for step, batch in enumerate(_UpperCAmelCase				):
                  # We need to skip steps until we reach the resumed step
                  if args.resume_from_checkpoint and epoch == 0:
                      if resume_step is not None and step < resume_step:
                          pass
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  lowerCAmelCase				:			List[Any]    			=      model(**_UpperCAmelCase				)
                  lowerCAmelCase				:			str    			=      outputs.loss
                  lowerCAmelCase				:			List[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(_UpperCAmelCase				)
                  if step % gradient_accumulation_steps == 0:
                      optimizer.step()
                      lr_scheduler.step()
                      optimizer.zero_grad()
                      # accelerator.print(lr_scheduler.get_lr())
  
                  overall_step += 1
  
                  if isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
                      lowerCAmelCase				:			Optional[int]    			=      f"step_{overall_step}"
                      if overall_step % checkpointing_steps == 0:
                          if args.output_dir is not None:
                              lowerCAmelCase				:			List[str]    			=      os.path.join(args.output_dir,      _UpperCAmelCase				)
                          accelerator.save_state(_UpperCAmelCase				)
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin				)				)				)
          accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used				)				)
          accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked				)				)
          accelerator.print(
              'Total Peak Memory consumed during the train (max): {}'.format(
                  tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
              accelerator.log(
                  {
                      'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin				),
                  },      step=_UpperCAmelCase,      )
  
          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
              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():
                      lowerCAmelCase				:			List[Any]    			=      model(**_UpperCAmelCase				)
                  lowerCAmelCase				:			Optional[Any]    			=      outputs.logits.argmax(dim=-1				)
                  lowerCAmelCase			,  lowerCAmelCase				:			Union[str, Any]    			=      accelerator.gather_for_metrics((predictions, batch['labels'])				)
                  metric.add_batch(
                      predictions=_UpperCAmelCase,      references=_UpperCAmelCase,      )
  
              lowerCAmelCase				:			Dict    			=      metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:",      _UpperCAmelCase				)
              if args.with_tracking:
                  accelerator.log(
                      {
                          'accuracy': eval_metric['accuracy'],
                          'f1': eval_metric['f1'],
                          'train_loss': total_loss.item() / len(_UpperCAmelCase				),
                      },      step=_UpperCAmelCase,      )
  
              if checkpointing_steps == "epoch":
                  lowerCAmelCase				:			str    			=      f"epoch_{epoch}"
                  if args.output_dir is not None:
                      lowerCAmelCase				:			List[str]    			=      os.path.join(args.output_dir,      _UpperCAmelCase				)
                  accelerator.save_state(_UpperCAmelCase				)
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin				)				)				)
          accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used				)				)
          accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked				)				)
          accelerator.print(
              'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
              accelerator.log(
                  {
                      'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin				),
                  },      step=_UpperCAmelCase,      )
  
      if args.with_tracking:
          accelerator.end_training()
  def 		SCREAMING_SNAKE_CASE__				(							)					-> Union[str, Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Dict    			=      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.'				)
      parser.add_argument(
          '--checkpointing_steps',      type=_UpperCAmelCase,      default=_UpperCAmelCase,      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=_UpperCAmelCase,      default=_UpperCAmelCase,      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=_UpperCAmelCase,      default='.',      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',      )
      parser.add_argument(
          '--logging_dir',      type=_UpperCAmelCase,      default='logs',      help='Location on where to store experiment tracking logs`',      )
      parser.add_argument(
          '--model_name_or_path',      type=_UpperCAmelCase,      help='Path to pretrained model or model identifier from huggingface.co/models.',      required=_UpperCAmelCase,      )
      lowerCAmelCase				:			Union[str, Any]    			=      parser.parse_args()
      lowerCAmelCase				:			Any    			=      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(_UpperCAmelCase,      _UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __A   :     Dict      =			{}
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[Any]      =			['''BarthezTokenizer''']
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[str]      =			['''BarthezTokenizerFast''']
  
  
  if TYPE_CHECKING:
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez import BarthezTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
   import sys
  
   __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  