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_														=      1_6
  A_														=      3_2
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					return int(x / 2**20      )
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __enter__(	self     :     int					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								gc.collect()
  								torch.cuda.empty_cache()
  								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  								SCREAMING_SNAKE_CASE_	      =       torch.cuda.memory_allocated()
  								SCREAMING_SNAKE_CASE_	      =       psutil.Process()
  
  								SCREAMING_SNAKE_CASE_	      =       self.cpu_mem_used()
  								SCREAMING_SNAKE_CASE_	      =       True
  								SCREAMING_SNAKE_CASE_	      =       threading.Thread(target=self.peak_monitor_func					)
  								SCREAMING_SNAKE_CASE_	      =       True
  								peak_monitor_thread.start()
  								return self
  
  			def _lowercase (	self     :     Any					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.process.memory_info().rss
  
  			def _lowercase (	self     :     Tuple					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       -1
  
  								while True:
  													SCREAMING_SNAKE_CASE_	      =       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]							,							*lowerCAmelCase_     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       False
  
  								gc.collect()
  								torch.cuda.empty_cache()
  								SCREAMING_SNAKE_CASE_	      =       torch.cuda.memory_allocated()
  								SCREAMING_SNAKE_CASE_	      =       torch.cuda.max_memory_allocated()
  								SCREAMING_SNAKE_CASE_	      =       bamb(self.end - self.begin					)
  								SCREAMING_SNAKE_CASE_	      =       bamb(self.peak - self.begin					)
  
  								SCREAMING_SNAKE_CASE_	      =       self.cpu_mem_used()
  								SCREAMING_SNAKE_CASE_	      =       bamb(self.cpu_end - self.cpu_begin					)
  								SCREAMING_SNAKE_CASE_	      =       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_														=      mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,UpperCAmelCase      ) == "1":
  										SCREAMING_SNAKE_CASE_	      =       2
  
  					# New Code #
  					# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  					SCREAMING_SNAKE_CASE_	      =       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:
  										SCREAMING_SNAKE_CASE_	      =       Accelerator(
  										    cpu=args.cpu				,mixed_precision=args.mixed_precision				,log_with='''wandb'''				,project_dir=args.logging_dir				,fsdp_plugin=UpperCAmelCase				,)
  					else:
  										SCREAMING_SNAKE_CASE_	      =       Accelerator(fsdp_plugin=UpperCAmelCase      )
  					accelerator.print(accelerator.distributed_type      )
  
  					if hasattr(args.checkpointing_steps				,'''isdigit'''      ):
  										if args.checkpointing_steps == "epoch":
  															SCREAMING_SNAKE_CASE_	      =       args.checkpointing_steps
  										elif args.checkpointing_steps.isdigit():
  															SCREAMING_SNAKE_CASE_	      =       int(args.checkpointing_steps      )
  										else:
  															raise ValueError(
  															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
  					else:
  										SCREAMING_SNAKE_CASE_	      =       None
  					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  					SCREAMING_SNAKE_CASE_	      =       config['''lr''']
  					SCREAMING_SNAKE_CASE_	      =       int(config['''num_epochs''']      )
  					SCREAMING_SNAKE_CASE_	      =       int(config['''seed''']      )
  					SCREAMING_SNAKE_CASE_	      =       int(config['''batch_size''']      )
  
  					# We need to initialize the trackers we use, and also store our configuration
  					if args.with_tracking:
  										SCREAMING_SNAKE_CASE_	      =       vars(UpperCAmelCase      )
  										accelerator.init_trackers('''fsdp_glue_no_trainer'''				,UpperCAmelCase      )
  
  					SCREAMING_SNAKE_CASE_	      =       AutoTokenizer.from_pretrained(args.model_name_or_path      )
  					SCREAMING_SNAKE_CASE_	      =       load_dataset('''glue'''				,'''mrpc'''      )
  					SCREAMING_SNAKE_CASE_	      =       evaluate.load('''glue'''				,'''mrpc'''      )
  
  					def tokenize_function(UpperCAmelCase      ):
  										# max_length=None => use the model max length (it's actually the default)
  										SCREAMING_SNAKE_CASE_	      =       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():
  										SCREAMING_SNAKE_CASE_	      =       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
  					SCREAMING_SNAKE_CASE_	      =       tokenized_datasets.rename_column('''label'''				,'''labels'''      )
  
  					# If the batch size is too big we use gradient accumulation
  					SCREAMING_SNAKE_CASE_	      =       1
  					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  										SCREAMING_SNAKE_CASE_	      =       batch_size // MAX_GPU_BATCH_SIZE
  										SCREAMING_SNAKE_CASE_	      =       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.
  										SCREAMING_SNAKE_CASE_	      =       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":
  															SCREAMING_SNAKE_CASE_	      =       16
  										elif accelerator.mixed_precision != "no":
  															SCREAMING_SNAKE_CASE_	      =       8
  										else:
  															SCREAMING_SNAKE_CASE_	      =       None
  
  										return tokenizer.pad(
  										    UpperCAmelCase				,padding='''longest'''				,max_length=UpperCAmelCase				,pad_to_multiple_of=UpperCAmelCase				,return_tensors='''pt'''				,)
  
  					# Instantiate dataloaders.
  					SCREAMING_SNAKE_CASE_	      =       DataLoader(
  					    tokenized_datasets['''train''']				,shuffle=UpperCAmelCase				,collate_fn=UpperCAmelCase				,batch_size=UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       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)
  					SCREAMING_SNAKE_CASE_	      =       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
  					SCREAMING_SNAKE_CASE_	      =       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
  					SCREAMING_SNAKE_CASE_	      =       torch.optim.AdamW(params=model.parameters()				,lr=UpperCAmelCase				,weight_decay=2E-4      )
  
  					# Instantiate scheduler
  					SCREAMING_SNAKE_CASE_	      =       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.
  					SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       accelerator.prepare(
  					    UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )
  
  					SCREAMING_SNAKE_CASE_	      =       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      )
  															SCREAMING_SNAKE_CASE_	      =       os.path.basename(args.resume_from_checkpoint      )
  										else:
  															# Get the most recent checkpoint
  															SCREAMING_SNAKE_CASE_	      =       [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
  															dirs.sort(key=os.path.getctime      )
  															SCREAMING_SNAKE_CASE_	      =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  										# Extract `epoch_{i}` or `step_{i}`
  										SCREAMING_SNAKE_CASE_	      =       os.path.splitext(UpperCAmelCase      )[0]
  
  										if "epoch" in training_difference:
  															num_epochs -= int(training_difference.replace('''epoch_'''				,''''''      )      )
  															SCREAMING_SNAKE_CASE_	      =       None
  										else:
  															SCREAMING_SNAKE_CASE_	      =       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
  															SCREAMING_SNAKE_CASE_	      =       (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:
  																				SCREAMING_SNAKE_CASE_	      =       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      )
  																				SCREAMING_SNAKE_CASE_	      =       model(**UpperCAmelCase      )
  																				SCREAMING_SNAKE_CASE_	      =       outputs.loss
  																				SCREAMING_SNAKE_CASE_	      =       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      ):
  																									SCREAMING_SNAKE_CASE_	      =       f'''step_{overall_step}'''
  																									if overall_step % checkpointing_steps == 0:
  																														if args.output_dir is not None:
  																																			SCREAMING_SNAKE_CASE_	      =       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():
  																									SCREAMING_SNAKE_CASE_	      =       model(**UpperCAmelCase      )
  																				SCREAMING_SNAKE_CASE_	      =       outputs.logits.argmax(dim=-1      )
  																				SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
  																				metric.add_batch(
  																				    predictions=UpperCAmelCase				,references=UpperCAmelCase				,)
  
  															SCREAMING_SNAKE_CASE_	      =       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":
  																				SCREAMING_SNAKE_CASE_	      =       f'''epoch_{epoch}'''
  																				if args.output_dir is not None:
  																									SCREAMING_SNAKE_CASE_	      =       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				(     )->		List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       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				,)
  					SCREAMING_SNAKE_CASE_	      =       parser.parse_args()
  					SCREAMING_SNAKE_CASE_	      =       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  					training_function(UpperCAmelCase				,UpperCAmelCase      )
  
  
  if __name__ == "__main__":
  		main()
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       value
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       int
  
  			def __init__(	self     :     Union[str, Any]					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       0
  
  			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return
  
  								if path_sum == self.target:
  													self.paths += 1
  
  								if node.left:
  													self.depth_first_search(node.left							,							path_sum + node.left.value					)
  								if node.right:
  													self.depth_first_search(node.right							,							path_sum + node.right.value					)
  
  
  
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int | None = None					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return 0
  								if target is not None:
  													SCREAMING_SNAKE_CASE_	      =       target
  
  								self.depth_first_search(lowerCAmelCase_							,							node.value					)
  								self.path_sum(node.left					)
  								self.path_sum(node.right					)
  
  								return self.paths
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  