code: import argparse
  import os
  
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator, DistributedType
  
  
  ########################################################################
  # 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)
  #
  # 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__    :       Optional[int]						=			1_6
  lowercase__    :       Tuple						=			3_2
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
  							# Initialize accelerator
  							if args.with_tracking:
  														lowerCAmelCase				=	Accelerator(
  														    cpu=args.cpu     ,						mixed_precision=args.mixed_precision     ,						log_with='''all'''     ,						project_dir=args.project_dir							)
  							else:
  														lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)
  
  							if hasattr(args.checkpointing_steps     ,						'''isdigit'''							):
  														if args.checkpointing_steps == "epoch":
  																					lowerCAmelCase				=	args.checkpointing_steps
  														elif args.checkpointing_steps.isdigit():
  																					lowerCAmelCase				=	int(args.checkpointing_steps							)
  														else:
  																					raise ValueError(
  																					    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."							)
  							else:
  														lowerCAmelCase				=	None
  							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  							lowerCAmelCase				=	config['''lr''']
  							lowerCAmelCase				=	int(config['''num_epochs''']							)
  							lowerCAmelCase				=	int(config['''seed''']							)
  							lowerCAmelCase				=	int(config['''batch_size''']							)
  
  							# We need to initialize the trackers we use, and also store our configuration
  							if args.with_tracking:
  														lowerCAmelCase				=	os.path.split(snake_case__							)[-1].split('''.'''							)[0]
  														accelerator.init_trackers(snake_case__     ,						snake_case__							)
  
  							lowerCAmelCase				=	AutoTokenizer.from_pretrained('''bert-base-cased'''							)
  							lowerCAmelCase				=	load_dataset('''glue'''     ,						'''mrpc'''							)
  							lowerCAmelCase				=	evaluate.load('''glue'''     ,						'''mrpc'''							)
  
  							def tokenize_function(snake_case__							):
  														# max_length=None => use the model max length (it's actually the default)
  														lowerCAmelCase				=	tokenizer(examples['''sentence1''']     ,						examples['''sentence2''']     ,						truncation=snake_case__     ,						max_length=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():
  														lowerCAmelCase				=	datasets.map(
  														    snake_case__     ,						batched=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
  							lowerCAmelCase				=	tokenized_datasets.rename_column('''label'''     ,						'''labels'''							)
  
  							# If the batch size is too big we use gradient accumulation
  							lowerCAmelCase				=	1
  							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  														lowerCAmelCase				=	batch_size // MAX_GPU_BATCH_SIZE
  														lowerCAmelCase				=	MAX_GPU_BATCH_SIZE
  
  							def collate_fn(snake_case__							):
  														# On TPU it's best to pad everything to the same length or training will be very slow.
  														lowerCAmelCase				=	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  														# When using mixed precision we want round multiples of 8/16
  														if accelerator.mixed_precision == "fp8":
  																					lowerCAmelCase				=	1_6
  														elif accelerator.mixed_precision != "no":
  																					lowerCAmelCase				=	8
  														else:
  																					lowerCAmelCase				=	None
  
  														return tokenizer.pad(
  														    snake_case__     ,						padding='''longest'''     ,						max_length=snake_case__     ,						pad_to_multiple_of=snake_case__     ,						return_tensors='''pt'''     ,						)
  
  							# Instantiate dataloaders.
  							lowerCAmelCase				=	DataLoader(
  							    tokenized_datasets['''train''']     ,						shuffle=snake_case__     ,						collate_fn=snake_case__     ,						batch_size=snake_case__							)
  							lowerCAmelCase				=	DataLoader(
  							    tokenized_datasets['''validation''']     ,						shuffle=snake_case__     ,						collate_fn=snake_case__     ,						batch_size=snake_case__							)
  
  							set_seed(snake_case__							)
  
  							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  							lowerCAmelCase				=	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,						return_dict=snake_case__							)
  
  							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  							lowerCAmelCase				=	model.to(accelerator.device							)
  
  							# Instantiate optimizer
  							lowerCAmelCase				=	AdamW(params=model.parameters()     ,						lr=snake_case__							)
  
  							# Instantiate scheduler
  							lowerCAmelCase				=	get_linear_schedule_with_warmup(
  							    optimizer=snake_case__     ,						num_warmup_steps=1_0_0     ,						num_training_steps=(len(snake_case__							) * num_epochs) // gradient_accumulation_steps     ,						)
  
  							# Prepare everything
  							# 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.
  							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	accelerator.prepare(
  							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)
  
  							# We need to keep track of how many total steps we have iterated over
  							lowerCAmelCase				=	0
  							# We also need to keep track of the stating epoch so files are named properly
  							lowerCAmelCase				=	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							)
  																					lowerCAmelCase				=	os.path.basename(args.resume_from_checkpoint							)
  														else:
  																					# Get the most recent checkpoint
  																					lowerCAmelCase				=	[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
  																					dirs.sort(key=os.path.getctime							)
  																					lowerCAmelCase				=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  														# Extract `epoch_{i}` or `step_{i}`
  														lowerCAmelCase				=	os.path.splitext(snake_case__							)[0]
  
  														if "epoch" in training_difference:
  																					lowerCAmelCase				=	int(training_difference.replace('''epoch_'''     ,						''''''							)							) + 1
  																					lowerCAmelCase				=	None
  														else:
  																					lowerCAmelCase				=	int(training_difference.replace('''step_'''     ,						''''''							)							)
  																					lowerCAmelCase				=	resume_step // len(snake_case__							)
  																					resume_step -= starting_epoch * len(snake_case__							)
  
      # Now we train the model
  							for epoch in range(snake_case__     ,						snake_case__							):
  														model.train()
  														if args.with_tracking:
  																					lowerCAmelCase				=	0
  														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
  																					# We need to skip steps until we reach the resumed step
  																					lowerCAmelCase				=	accelerator.skip_first_batches(snake_case__     ,						snake_case__							)
  																					overall_step += resume_step
  														else:
  																					# After the first iteration though, we need to go back to the original dataloader
  																					lowerCAmelCase				=	train_dataloader
  														for step, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					batch.to(accelerator.device							)
  																					lowerCAmelCase				=	model(**snake_case__							)
  																					lowerCAmelCase				=	outputs.loss
  																					lowerCAmelCase				=	loss / gradient_accumulation_steps
  																					# We keep track of the loss at each epoch
  																					if args.with_tracking:
  																												total_loss += loss.detach().float()
  																					accelerator.backward(snake_case__							)
  																					if step % gradient_accumulation_steps == 0:
  																												optimizer.step()
  																												lr_scheduler.step()
  																												optimizer.zero_grad()
  
  																					overall_step += 1
  
  																					if isinstance(snake_case__     ,						snake_case__							):
  																												lowerCAmelCase				=	f"step_{overall_step}"
  																												if overall_step % checkpointing_steps == 0:
  																																			if args.output_dir is not None:
  																																										lowerCAmelCase				=	os.path.join(args.output_dir     ,						snake_case__							)
  																																			accelerator.save_state(snake_case__							)
  
  														model.eval()
  														for step, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					batch.to(accelerator.device							)
  																					with torch.no_grad():
  																												lowerCAmelCase				=	model(**snake_case__							)
  																					lowerCAmelCase				=	outputs.logits.argmax(dim=-1							)
  																					lowerCAmelCase	,   lowerCAmelCase				=	accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
  																					metric.add_batch(
  																					    predictions=snake_case__     ,						references=snake_case__     ,						)
  
  														lowerCAmelCase				=	metric.compute()
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f"epoch {epoch}:"     ,						snake_case__							)
  														if args.with_tracking:
  																					accelerator.log(
  																					    {
  																					        '''accuracy''': eval_metric['''accuracy'''],
  																					        '''f1''': eval_metric['''f1'''],
  																					        '''train_loss''': total_loss.item() / len(snake_case__							),
  																					        '''epoch''': epoch,
  																					    }     ,						step=snake_case__     ,						)
  
  														if checkpointing_steps == "epoch":
  																					lowerCAmelCase				=	f"epoch_{epoch}"
  																					if args.output_dir is not None:
  																												lowerCAmelCase				=	os.path.join(args.output_dir     ,						snake_case__							)
  																					accelerator.save_state(snake_case__							)
  
  							if args.with_tracking:
  														accelerator.end_training()
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
  							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
  							parser.add_argument(
  							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=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=snake_case__     ,						default=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=snake_case__     ,						default=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=snake_case__     ,						default='''.'''     ,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,						)
  							parser.add_argument(
  							    '''--project_dir'''     ,						type=snake_case__     ,						default='''logs'''     ,						help='''Location on where to store experiment tracking logs` and relevent project information'''     ,						)
  							lowerCAmelCase				=	parser.parse_args()
  							lowerCAmelCase				=	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
  							training_function(snake_case__     ,						snake_case__							)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
style_context: from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      str     =       24
  	UpperCAmelCase_						:      Dict     =       21
  	UpperCAmelCase_						:      List[str]     =       18
  	UpperCAmelCase_						:      Optional[int]     =       15
  	UpperCAmelCase_						:      str     =       12
  	UpperCAmelCase_						:      Union[str, Any]     =       9
  	UpperCAmelCase_						:      List[Any]     =       6
  	UpperCAmelCase_						:      Any     =       3
  	UpperCAmelCase_						:      List[str]     =       2
  	UpperCAmelCase_						:      Any     =       1
  	UpperCAmelCase_						:      Any     =       -1
  	UpperCAmelCase_						:      Any     =       -2
  	UpperCAmelCase_						:      Optional[int]     =       -3
  	UpperCAmelCase_						:      List[Any]     =       -6
  	UpperCAmelCase_						:      Optional[int]     =       -9
  	UpperCAmelCase_						:      str     =       -12
  	UpperCAmelCase_						:      Union[str, Any]     =       -15
  	UpperCAmelCase_						:      List[str]     =       -18
  	UpperCAmelCase_						:      str     =       -21
  	UpperCAmelCase_						:      Dict     =       -24
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      List[str]     =       8
  	UpperCAmelCase_						:      Union[str, Any]     =       7
  	UpperCAmelCase_						:      int     =       6
  	UpperCAmelCase_						:      int     =       5
  	UpperCAmelCase_						:      Any     =       4
  	UpperCAmelCase_						:      Dict     =       3
  	UpperCAmelCase_						:      Tuple     =       2
  	UpperCAmelCase_						:      Any     =       1
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						)  ->			float:
  							if isinstance(snake_case__     ,						snake_case__							):
  														lowerCAmelCase				=	SIUnit[known_prefix.lower()]
  							if isinstance(snake_case__     ,						snake_case__							):
  														lowerCAmelCase				=	SIUnit[unknown_prefix.lower()]
  							lowerCAmelCase				=	known_amount * (
  							    1_0 ** (known_prefix.value - unknown_prefix.value)
  							)
  							return unknown_amount
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						)  ->			float:
  							if isinstance(snake_case__     ,						snake_case__							):
  														lowerCAmelCase				=	BinaryUnit[known_prefix.lower()]
  							if isinstance(snake_case__     ,						snake_case__							):
  														lowerCAmelCase				=	BinaryUnit[unknown_prefix.lower()]
  							lowerCAmelCase				=	known_amount * (
  							    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
  							)
  							return unknown_amount
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  