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_    :					Union[str, Any]             =16
  A_    :					List[str]             =32
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return int(x / 2**20      )
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __enter__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													gc.collect()
  													torch.cuda.empty_cache()
  													torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  													a_					=		torch.cuda.memory_allocated()
  													a_					=		psutil.Process()
  
  													a_					=		self.cpu_mem_used()
  													a_					=		True
  													a_					=		threading.Thread(target=self.peak_monitor_func		)
  													a_					=		True
  													peak_monitor_thread.start()
  													return self
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.process.memory_info().rss
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		-1
  
  													while True:
  																			a_					=		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			,    *_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		False
  
  													gc.collect()
  													torch.cuda.empty_cache()
  													a_					=		torch.cuda.memory_allocated()
  													a_					=		torch.cuda.max_memory_allocated()
  													a_					=		bamb(self.end - self.begin		)
  													a_					=		bamb(self.peak - self.begin		)
  
  													a_					=		self.cpu_mem_used()
  													a_					=		bamb(self.cpu_end - self.cpu_begin		)
  													a_					=		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_    :					List[Any]             =mocked_dataloaders  # noqa: F811
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,    UpperCAmelCase__      ) == "1":
  												a_					=		2
  
  						# New Code #
  						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  						a_					=		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:
  												a_					=		Accelerator(
  												    cpu=args.cpu       ,    mixed_precision=args.mixed_precision       ,    log_with="""wandb"""       ,    project_dir=args.logging_dir       ,    fsdp_plugin=UpperCAmelCase__       ,    )
  						else:
  												a_					=		Accelerator(fsdp_plugin=UpperCAmelCase__      )
  						accelerator.print(accelerator.distributed_type      )
  
  						if hasattr(args.checkpointing_steps       ,    """isdigit"""      ):
  												if args.checkpointing_steps == "epoch":
  																		a_					=		args.checkpointing_steps
  												elif args.checkpointing_steps.isdigit():
  																		a_					=		int(args.checkpointing_steps      )
  												else:
  																		raise ValueError(
  																		    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
  						else:
  												a_					=		None
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						a_					=		config["""lr"""]
  						a_					=		int(config["""num_epochs"""]      )
  						a_					=		int(config["""seed"""]      )
  						a_					=		int(config["""batch_size"""]      )
  
  						# We need to initialize the trackers we use, and also store our configuration
  						if args.with_tracking:
  												a_					=		vars(UpperCAmelCase__      )
  												accelerator.init_trackers("""fsdp_glue_no_trainer"""       ,    UpperCAmelCase__      )
  
  						a_					=		AutoTokenizer.from_pretrained(args.model_name_or_path      )
  						a_					=		load_dataset("""glue"""       ,    """mrpc"""      )
  						a_					=		evaluate.load("""glue"""       ,    """mrpc"""      )
  
  						def tokenize_function(UpperCAmelCase__      ):
  												# max_length=None => use the model max length (it's actually the default)
  												a_					=		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():
  												a_					=		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
  						a_					=		tokenized_datasets.rename_column("""label"""       ,    """labels"""      )
  
  						# If the batch size is too big we use gradient accumulation
  						a_					=		1
  						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  												a_					=		batch_size // MAX_GPU_BATCH_SIZE
  												a_					=		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.
  												a_					=		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":
  																		a_					=		16
  												elif accelerator.mixed_precision != "no":
  																		a_					=		8
  												else:
  																		a_					=		None
  
  												return tokenizer.pad(
  												    UpperCAmelCase__       ,    padding="""longest"""       ,    max_length=UpperCAmelCase__       ,    pad_to_multiple_of=UpperCAmelCase__       ,    return_tensors="""pt"""       ,    )
  
  						# Instantiate dataloaders.
  						a_					=		DataLoader(
  						    tokenized_datasets["""train"""]       ,    shuffle=UpperCAmelCase__       ,    collate_fn=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__      )
  						a_					=		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)
  						a_					=		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
  						a_					=		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
  						a_					=		torch.optim.AdamW(params=model.parameters()       ,    lr=UpperCAmelCase__       ,    weight_decay=2e-4      )
  
  						# Instantiate scheduler
  						a_					=		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.
  						a_			,   a_			,   a_			,   a_					=		accelerator.prepare(
  						    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  						a_					=		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      )
  																		a_					=		os.path.basename(args.resume_from_checkpoint      )
  												else:
  																		# Get the most recent checkpoint
  																		a_					=		[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
  																		dirs.sort(key=os.path.getctime      )
  																		a_					=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  												# Extract `epoch_{i}` or `step_{i}`
  												a_					=		os.path.splitext(UpperCAmelCase__      )[0]
  
  												if "epoch" in training_difference:
  																		num_epochs -= int(training_difference.replace("""epoch_"""       ,    """"""      )      )
  																		a_					=		None
  												else:
  																		a_					=		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
  																		a_					=		(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:
  																								a_					=		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      )
  																								a_					=		model(**UpperCAmelCase__      )
  																								a_					=		outputs.loss
  																								a_					=		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__      ):
  																														a_					=		F"step_{overall_step}"
  																														if overall_step % checkpointing_steps == 0:
  																																				if args.output_dir is not None:
  																																										a_					=		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():
  																														a_					=		model(**UpperCAmelCase__      )
  																								a_					=		outputs.logits.argmax(dim=-1      )
  																								a_			,   a_					=		accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
  																								metric.add_batch(
  																								    predictions=UpperCAmelCase__       ,    references=UpperCAmelCase__       ,    )
  
  																		a_					=		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":
  																								a_					=		F"epoch_{epoch}"
  																								if args.output_dir is not None:
  																														a_					=		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   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		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__       ,    )
  						a_					=		parser.parse_args()
  						a_					=		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  						training_function(UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  
  if __name__ == "__main__":
  	main()
style_context: 
  
  
  import abc
  import copy
  import dataclasses
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Type, TypeVar
  
  from ..features import Features
  
  
  A_    :					Tuple             =TypeVar("""T""", bound="""TaskTemplate""")
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        lowercase_				( abc.ABC):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  							snake_case_        =		42
  							snake_case_        =		42
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return copy.deepcopy(self		)
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return Features(**self.input_schema			,    **self.label_schema		)
  
  
  							@property
  							@abc.abstractmethod
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													raise NotImplementedError
  
  
  
  
  
  
  							@classmethod
  							def 						lowercase__     (   cls			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		{f.name for f in dataclasses.fields(cls		)}
  													return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)