code: 
  '''simple docstring'''
  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
  #
  ########################################################################
  
  
  lowercase__						=			1_6
  lowercase__						=			3_2
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
    '''simple docstring'''
  
  
    return int(x / 2**20 )
  
  
  
  
  
  
  class   snake_case__					:
  
  
  
  
  
  
  
    """simple docstring"""
  
    def __enter__(			self							: Optional[int]			)	-> Optional[int]:
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      snake_case     :					int     =	torch.cuda.memory_allocated()
      snake_case     :					List[str]     =	psutil.Process()
  
      snake_case     :					Tuple     =	self.cpu_mem_used()
      snake_case     :					Union[str, Any]     =	True
      snake_case     :					Optional[int]     =	threading.Thread(target=self.peak_monitor_func			)
      snake_case     :					str     =	True
      peak_monitor_thread.start()
      return self
  
  
  
    def 			lowerCAmelCase							(			self							: List[str]			)	-> Union[str, Any]:
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      return self.process.memory_info().rss
  
  
  
    def 			lowerCAmelCase							(			self							: int			)	-> Union[str, Any]:
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      snake_case     :					Union[str, Any]     =	-1
  
      while True:
        snake_case     :					Tuple     =	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							: Tuple     ,  *UpperCamelCase__							: str			)	-> str:
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      snake_case     :					Tuple     =	False
  
      gc.collect()
      torch.cuda.empty_cache()
      snake_case     :					Dict     =	torch.cuda.memory_allocated()
      snake_case     :					Optional[Any]     =	torch.cuda.max_memory_allocated()
      snake_case     :					int     =	bamb(self.end - self.begin			)
      snake_case     :					Union[str, Any]     =	bamb(self.peak - self.begin			)
  
      snake_case     :					Optional[int]     =	self.cpu_mem_used()
      snake_case     :					List[str]     =	bamb(self.cpu_end - self.cpu_begin			)
      snake_case     :					str     =	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
  
        lowercase__						=			mocked_dataloaders  # noqa: F811
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
    '''simple docstring'''
  
  
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,					SCREAMING_SNAKE_CASE__ ) == "1":
      snake_case     :					List[str]     =	2
  
    # New Code #
    # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
    snake_case     :					List[Any]     =	FullyShardedDataParallelPlugin(
        state_dict_config=FullStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__   ,					ranka_only=SCREAMING_SNAKE_CASE__ )   ,					optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__   ,					ranka_only=SCREAMING_SNAKE_CASE__ )   ,					)
  
    # Initialize accelerator
    if args.with_tracking:
      snake_case     :					Optional[int]     =	Accelerator(
          cpu=args.cpu   ,					mixed_precision=args.mixed_precision   ,					log_with='''wandb'''   ,					project_dir=args.logging_dir   ,					fsdp_plugin=SCREAMING_SNAKE_CASE__   ,					)
    else:
      snake_case     :					str     =	Accelerator(fsdp_plugin=SCREAMING_SNAKE_CASE__ )
    accelerator.print(accelerator.distributed_type )
  
    if hasattr(args.checkpointing_steps   ,					'''isdigit''' ):
      if args.checkpointing_steps == "epoch":
        snake_case     :					Optional[int]     =	args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
        snake_case     :					Dict     =	int(args.checkpointing_steps )
      else:
        raise ValueError(
            F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
    else:
      snake_case     :					str     =	None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case     :					Optional[Any]     =	config['''lr''']
    snake_case     :					Optional[int]     =	int(config['''num_epochs'''] )
    snake_case     :					List[Any]     =	int(config['''seed'''] )
    snake_case     :					Tuple     =	int(config['''batch_size'''] )
  
    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
      snake_case     :					str     =	vars(SCREAMING_SNAKE_CASE__ )
      accelerator.init_trackers('''fsdp_glue_no_trainer'''   ,					SCREAMING_SNAKE_CASE__ )
  
    snake_case     :					Any     =	AutoTokenizer.from_pretrained(args.model_name_or_path )
    snake_case     :					List[Any]     =	load_dataset('''glue'''   ,					'''mrpc''' )
    snake_case     :					Any     =	evaluate.load('''glue'''   ,					'''mrpc''' )
  
    def tokenize_function(SCREAMING_SNAKE_CASE__ ):
      # max_length=None => use the model max length (it's actually the default)
      snake_case     :					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():
      snake_case     :					Dict     =	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
    snake_case     :					int     =	tokenized_datasets.rename_column('''label'''   ,					'''labels''' )
  
    # If the batch size is too big we use gradient accumulation
    snake_case     :					List[str]     =	1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      snake_case     :					Optional[int]     =	batch_size // MAX_GPU_BATCH_SIZE
      snake_case     :					List[Any]     =	MAX_GPU_BATCH_SIZE
  
    def collate_fn(SCREAMING_SNAKE_CASE__ ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      snake_case     :					Any     =	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":
        snake_case     :					Any     =	16
      elif accelerator.mixed_precision != "no":
        snake_case     :					List[Any]     =	8
      else:
        snake_case     :					List[str]     =	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.
    snake_case     :					Any     =	DataLoader(
        tokenized_datasets['''train''']   ,					shuffle=SCREAMING_SNAKE_CASE__   ,					collate_fn=SCREAMING_SNAKE_CASE__   ,					batch_size=SCREAMING_SNAKE_CASE__ )
    snake_case     :					Dict     =	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)
    snake_case     :					Optional[Any]     =	AutoModelForSequenceClassification.from_pretrained(
        args.model_name_or_path   ,					return_dict=SCREAMING_SNAKE_CASE__   ,					low_cpu_mem_usage=SCREAMING_SNAKE_CASE__ )
  
    # New Code #
    # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
    snake_case     :					Union[str, Any]     =	accelerator.prepare(SCREAMING_SNAKE_CASE__ )
    accelerator.print(SCREAMING_SNAKE_CASE__ )
  
    # 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
    snake_case     :					Optional[Any]     =	torch.optim.AdamW(params=model.parameters()   ,					lr=SCREAMING_SNAKE_CASE__   ,					weight_decay=2E-4 )
  
    # Instantiate scheduler
    snake_case     :					int     =	get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE__   ,					num_warmup_steps=10   ,					num_training_steps=(len(SCREAMING_SNAKE_CASE__ ) * 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.
    snake_case     :					str     =	accelerator.prepare(
        SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
    snake_case     :					Dict     =	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 )
        snake_case     :					Any     =	os.path.basename(args.resume_from_checkpoint )
      else:
        # Get the most recent checkpoint
        snake_case     :					List[Any]     =	[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
        dirs.sort(key=os.path.getctime )
        snake_case     :					List[Any]     =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      snake_case     :					Optional[Any]     =	os.path.splitext(SCREAMING_SNAKE_CASE__ )[0]
  
      if "epoch" in training_difference:
        num_epochs -= int(training_difference.replace('''epoch_'''   ,					'''''' ) )
        snake_case     :					Dict     =	None
      else:
        snake_case     :					Optional[int]     =	int(training_difference.replace('''step_'''   ,					'''''' ) )
        num_epochs -= resume_step // len(SCREAMING_SNAKE_CASE__ )
        # If resuming by step, we also need to know exactly how far into the DataLoader we went
        snake_case     :					str     =	(num_epochs * len(SCREAMING_SNAKE_CASE__ )) - resume_step
  
      # Now we train the model
    for epoch in range(SCREAMING_SNAKE_CASE__ ):
      # New Code #
      # context manager to track the peak memory usage during the training epoch
      with TorchTracemalloc() as tracemalloc:
        model.train()
        if args.with_tracking:
          snake_case     :					Any     =	0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
          # 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 )
          snake_case     :					str     =	model(**SCREAMING_SNAKE_CASE__ )
          snake_case     :					Optional[int]     =	outputs.loss
          snake_case     :					Tuple     =	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()
            # accelerator.print(lr_scheduler.get_lr())
  
          overall_step += 1
  
          if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
            snake_case     :					Dict     =	F'step_{overall_step}'
            if overall_step % checkpointing_steps == 0:
              if args.output_dir is not None:
                snake_case     :					Union[str, Any]     =	os.path.join(args.output_dir   ,					SCREAMING_SNAKE_CASE__ )
              accelerator.save_state(SCREAMING_SNAKE_CASE__ )
          # 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=SCREAMING_SNAKE_CASE__   ,					)
  
      # New Code #
      # context manager to track the peak memory usage during the evaluation
      with TorchTracemalloc() as tracemalloc:
        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():
            snake_case     :					Optional[Any]     =	model(**SCREAMING_SNAKE_CASE__ )
          snake_case     :					List[str]     =	outputs.logits.argmax(dim=-1 )
          snake_case     :					Tuple     =	accelerator.gather_for_metrics((predictions, batch['''labels''']) )
          metric.add_batch(
              predictions=SCREAMING_SNAKE_CASE__   ,					references=SCREAMING_SNAKE_CASE__   ,					)
  
        snake_case     :					List[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__ ),
              }   ,					step=SCREAMING_SNAKE_CASE__   ,					)
  
        if checkpointing_steps == "epoch":
          snake_case     :					Optional[Any]     =	F'epoch_{epoch}'
          if args.output_dir is not None:
            snake_case     :					int     =	os.path.join(args.output_dir   ,					SCREAMING_SNAKE_CASE__ )
          accelerator.save_state(SCREAMING_SNAKE_CASE__ )
          # 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=SCREAMING_SNAKE_CASE__   ,					)
  
    if args.with_tracking:
      accelerator.end_training()
  
  
  
  
  def       _UpperCamelCase	(					)     ->       List[str]:
  
  
    '''simple docstring'''
  
  
    snake_case     :					str     =	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(
        '''--logging_dir'''   ,					type=SCREAMING_SNAKE_CASE__   ,					default='''logs'''   ,					help='''Location on where to store experiment tracking logs`'''   ,					)
    parser.add_argument(
        '''--model_name_or_path'''   ,					type=SCREAMING_SNAKE_CASE__   ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,					required=SCREAMING_SNAKE_CASE__   ,					)
    snake_case     :					str     =	parser.parse_args()
    snake_case     :					Optional[Any]     =	{'''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: 
  '''simple docstring'''
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowercase__						=			{
      "configuration_flava": [
          "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "FlavaConfig",
          "FlavaImageCodebookConfig",
          "FlavaImageConfig",
          "FlavaMultimodalConfig",
          "FlavaTextConfig",
      ],
  }
  
  try:
  						if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			["FlavaFeatureExtractor"]
  						lowercase__						=			["FlavaImageProcessor"]
  						lowercase__						=			["FlavaProcessor"]
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			[
  						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "FlavaForPreTraining",
  						    "FlavaImageCodebook",
  						    "FlavaImageModel",
  						    "FlavaModel",
  						    "FlavaMultimodalModel",
  						    "FlavaPreTrainedModel",
  						    "FlavaTextModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_flava import (
  						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    FlavaConfig,
  						    FlavaImageCodebookConfig,
  						    FlavaImageConfig,
  						    FlavaMultimodalConfig,
  						    FlavaTextConfig,
  						)
  
  						try:
  												if not is_vision_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .feature_extraction_flava import FlavaFeatureExtractor
  												from .image_processing_flava import FlavaImageProcessor
  												from .processing_flava import FlavaProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_flava import (
  												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    FlavaForPreTraining,
  												    FlavaImageCodebook,
  												    FlavaImageModel,
  												    FlavaModel,
  												    FlavaMultimodalModel,
  												    FlavaPreTrainedModel,
  												    FlavaTextModel,
  												)
  
  else:
  						import sys
  
  						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  