code: import argparse
  import os
  
  # New Code #
  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
  from accelerate.utils import find_executable_batch_size
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing how to combine both the gradient accumulation
  # and automatic batch size finder utilities of Accelerate to perfrom
  # automatic gradient accumulation
  #
  # 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)
  #
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # 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__    :       Dict						=			3_2
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__ = 1_6							)  ->			Any:
  							lowerCAmelCase				=	AutoTokenizer.from_pretrained('''bert-base-cased'''							)
  							lowerCAmelCase				=	load_dataset('''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'''							)
  
  							def collate_fn(snake_case__							):
  														# 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'''     ,						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__							)
  
  							return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  	from accelerate.test_utils.training import mocked_dataloaders
  
  	lowercase__    :       Union[str, Any]						=			mocked_dataloaders  # noqa: F811
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
  							# For testing only
  							if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,						snake_case__							) == "1":
  														lowerCAmelCase				=	2
  							# Initialize accelerator
  							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)
  							# 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''']							)
  
  							lowerCAmelCase				=	evaluate.load('''glue'''     ,						'''mrpc'''							)
  
  							# New Code #
  							# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  							# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  							# half each time. From this, we can calculate the number of gradient accumulation steps needed
  							# and modify the Accelerator object as a result
  							@find_executable_batch_size(starting_batch_size=int(snake_case__							)							)
  							def inner_training_loop(snake_case__							):
  														# Since we need to modify the outside accelerator object, we need to bring it
  														# to the local scope
  														nonlocal accelerator
  
  														# We can calculate the number of gradient accumulation steps based on the current
  														# batch size vs the starting batch size
  														lowerCAmelCase				=	observed_batch_size // batch_size
  
  														# And then set it in the Accelerator directly:
  														lowerCAmelCase				=	num_gradient_accumulation_steps
  
  														# Next we need to free all of the stored model references in the Accelerator each time
  														accelerator.free_memory()
  
  														# And set the seed so our results are reproducable each reset
  														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__							)
  														lowerCAmelCase				=	get_dataloaders(snake_case__     ,						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)     ,						)
  
  														# 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				=	accelerator.prepare(
  														    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)
  
  														# Now we train the model
  														for epoch in range(snake_case__							):
  																					model.train()
  																					for step, batch in enumerate(snake_case__							):
  																												# And perform gradient accumulation
  																												with accelerator.accumulate(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
  																																			accelerator.backward(snake_case__							)
  																																			optimizer.step()
  																																			lr_scheduler.step()
  																																			optimizer.zero_grad()
  
  																					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				=	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__							)
  
      # New Code #
      # And call it at the end with no arguments
      # Note: You could also refactor this outside of your training loop function
  							inner_training_loop()
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			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.'''							)
  							lowerCAmelCase				=	parser.parse_args()
  							# New Code #
  							# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  							lowerCAmelCase				=	{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 2_5_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()
  
  
  
  