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 ,snake_case ,snake_case ,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 ,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'''
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		for a in range(300 ):
  				for b in range(a + 1   ,					400 ):
  						for c in range(b + 1   ,					500 ):
  								if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
  										return a * b * c
  
  		return -1
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		for a in range(300 ):
  				for b in range(400 ):
  						snake_case     :					Dict     =	1000 - a - b
  						if a < b < c and (a**2) + (b**2) == (c**2):
  								return a * b * c
  
  		return -1
  
  
  
  
  def       _UpperCamelCase	(					)     ->       None:
  
  
  		'''simple docstring'''
  
  
  		import timeit
  
  		print(
  		    timeit.timeit('''solution()'''   ,					setup='''from __main__ import solution'''   ,					number=1000 ) )
  		print(
  		    timeit.timeit(
  		        '''solution_fast()'''   ,					setup='''from __main__ import solution_fast'''   ,					number=1000 ) )
  
  
  if __name__ == "__main__":
  						print(f"{solution() = }")
  
  
  