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
  #
  ########################################################################
  
  
  UpperCamelCase__				       =			16
  UpperCamelCase__				       =			32
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return int(x / 2**20			)
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       def __enter__( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              gc.collect()
              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
              _lowercase					:				Dict           =			torch.cuda.memory_allocated()
              _lowercase					:				Union[str, Any]           =			psutil.Process()
  
              _lowercase					:				List[Any]           =			self.cpu_mem_used()
              _lowercase					:				int           =			True
              _lowercase					:				Optional[int]           =			threading.Thread(target=self.peak_monitor_func		)
              _lowercase					:				List[str]           =			True
              peak_monitor_thread.start()
              return self
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return self.process.memory_info().rss
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			-1
  
              while True:
                     _lowercase					:				List[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       :       Optional[Any]	,      *UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			False
  
              gc.collect()
              torch.cuda.empty_cache()
              _lowercase					:				str           =			torch.cuda.memory_allocated()
              _lowercase					:				int           =			torch.cuda.max_memory_allocated()
              _lowercase					:				int           =			bamb(self.end - self.begin		)
              _lowercase					:				List[str]           =			bamb(self.peak - self.begin		)
  
              _lowercase					:				List[Any]           =			self.cpu_mem_used()
              _lowercase					:				List[str]           =			bamb(self.cpu_end - self.cpu_begin		)
              _lowercase					:				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
  
     UpperCamelCase__				       =			mocked_dataloaders  # noqa: F811
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,     UpperCAmelCase_			) == "1":
                _lowercase					:				Optional[int]           =			2
  
         # New Code #
         # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
         _lowercase					:				Any           =			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:
                _lowercase					:				Union[str, Any]           =			Accelerator(
                    cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with='''wandb'''    ,     project_dir=args.logging_dir    ,     fsdp_plugin=UpperCAmelCase_    ,     )
         else:
                _lowercase					:				Optional[Any]           =			Accelerator(fsdp_plugin=UpperCAmelCase_			)
         accelerator.print(accelerator.distributed_type			)
  
         if hasattr(args.checkpointing_steps    ,     '''isdigit'''			):
                if args.checkpointing_steps == "epoch":
                       _lowercase					:				str           =			args.checkpointing_steps
                elif args.checkpointing_steps.isdigit():
                       _lowercase					:				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:
                _lowercase					:				List[Any]           =			None
         # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
         _lowercase					:				Any           =			config['''lr''']
         _lowercase					:				str           =			int(config['''num_epochs''']			)
         _lowercase					:				Union[str, Any]           =			int(config['''seed''']			)
         _lowercase					:				Tuple           =			int(config['''batch_size''']			)
  
         # We need to initialize the trackers we use, and also store our configuration
         if args.with_tracking:
                _lowercase					:				int           =			vars(UpperCAmelCase_			)
                accelerator.init_trackers('''fsdp_glue_no_trainer'''    ,     UpperCAmelCase_			)
  
         _lowercase					:				int           =			AutoTokenizer.from_pretrained(args.model_name_or_path			)
         _lowercase					:				List[Any]           =			load_dataset('''glue'''    ,     '''mrpc'''			)
         _lowercase					:				List[Any]           =			evaluate.load('''glue'''    ,     '''mrpc'''			)
  
         def tokenize_function(UpperCAmelCase_			):
                # max_length=None => use the model max length (it's actually the default)
                _lowercase					:				Any           =			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					:				Tuple           =			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[Any]           =			tokenized_datasets.rename_column('''label'''    ,     '''labels'''			)
  
         # If the batch size is too big we use gradient accumulation
         _lowercase					:				List[str]           =			1
         if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
                _lowercase					:				Dict           =			batch_size // MAX_GPU_BATCH_SIZE
                _lowercase					:				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.
                _lowercase					:				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":
                       _lowercase					:				str           =			16
                elif accelerator.mixed_precision != "no":
                       _lowercase					:				List[str]           =			8
                else:
                       _lowercase					:				Union[str, Any]           =			None
  
                return tokenizer.pad(
                    UpperCAmelCase_    ,     padding='''longest'''    ,     max_length=UpperCAmelCase_    ,     pad_to_multiple_of=UpperCAmelCase_    ,     return_tensors='''pt'''    ,     )
  
         # Instantiate dataloaders.
         _lowercase					:				Optional[int]           =			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_			)
  
         set_seed(UpperCAmelCase_			)
  
         # Instantiate the model (we build the model here so that the seed also control new weights initialization)
         _lowercase					:				Union[str, Any]           =			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
         _lowercase					:				Any           =			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
         _lowercase					:				str           =			torch.optim.AdamW(params=model.parameters()    ,     lr=UpperCAmelCase_    ,     weight_decay=2E-4			)
  
         # Instantiate scheduler
         _lowercase					:				List[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.
         _lowercase					:				List[Any]           =			accelerator.prepare(
             UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
  
         _lowercase					:				str           =			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					:				List[Any]           =			os.path.basename(args.resume_from_checkpoint			)
                else:
                       # Get the most recent checkpoint
                       _lowercase					:				str           =			[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
                       dirs.sort(key=os.path.getctime			)
                       _lowercase					:				str           =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
                # Extract `epoch_{i}` or `step_{i}`
                _lowercase					:				Any           =			os.path.splitext(UpperCAmelCase_			)[0]
  
                if "epoch" in training_difference:
                       num_epochs -= int(training_difference.replace('''epoch_'''    ,     ''''''			)			)
                       _lowercase					:				Optional[int]           =			None
                else:
                       _lowercase					:				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
                       _lowercase					:				Union[str, Any]           =			(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:
                              _lowercase					:				Optional[int]           =			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			)
                              _lowercase					:				Optional[Any]           =			model(**UpperCAmelCase_			)
                              _lowercase					:				int           =			outputs.loss
                              _lowercase					:				int           =			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_			):
                                     _lowercase					:				str           =			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    ,     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():
                                     _lowercase					:				Tuple           =			model(**UpperCAmelCase_			)
                              _lowercase					:				Union[str, Any]           =			outputs.logits.argmax(dim=-1			)
                              _lowercase					:				List[str]           =			accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
                              metric.add_batch(
                                  predictions=UpperCAmelCase_    ,     references=UpperCAmelCase_    ,     )
  
                       _lowercase					:				Union[str, Any]           =			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":
                              _lowercase					:				List[str]           =			F'epoch_{epoch}'
                              if args.output_dir is not None:
                                     _lowercase					:				int           =			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   UpperCamelCase__   (    )						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				str           =			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_    ,     )
         _lowercase					:				Dict           =			parser.parse_args()
         _lowercase					:				List[str]           =			{'''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_tokenizers_available, is_torch_available
  
  
  UpperCamelCase__				       =			{
      'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
      'tokenization_realm': ['RealmTokenizer'],
  }
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			['RealmTokenizerFast']
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RealmEmbedder',
         'RealmForOpenQA',
         'RealmKnowledgeAugEncoder',
         'RealmPreTrainedModel',
         'RealmReader',
         'RealmScorer',
         'load_tf_weights_in_realm',
     ]
     UpperCamelCase__				       =			['RealmRetriever']
  
  
  if TYPE_CHECKING:
     from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
     from .tokenization_realm import RealmTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_realm import RealmTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_realm import (
            REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RealmEmbedder,
            RealmForOpenQA,
            RealmKnowledgeAugEncoder,
            RealmPreTrainedModel,
            RealmReader,
            RealmScorer,
            load_tf_weights_in_realm,
        )
        from .retrieval_realm import RealmRetriever
  
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)