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
  #
  ########################################################################
  
  UpperCAmelCase      :						List[Any]    =		32
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Accelerator     ,       lowerCamelCase__		:	int = 16			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
  			lowerCamelCase								=     load_dataset("""glue"""     ,       """mrpc"""			)
  
  			def tokenize_function(lowerCamelCase__		:	str			):
  						# max_length=None => use the model max length (it's actually the default)
  						lowerCamelCase								=     tokenizer(examples["""sentence1"""]     ,       examples["""sentence2"""]     ,       truncation=lowerCamelCase__     ,       max_length=lowerCamelCase__			)
  						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(
  						    lowerCamelCase__     ,       batched=lowerCamelCase__     ,       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(lowerCamelCase__		:	str			):
  						# When using mixed precision we want round multiples of 8/16
  						if accelerator.mixed_precision == "fp8":
  									lowerCamelCase								=     16
  						elif accelerator.mixed_precision != "no":
  									lowerCamelCase								=     8
  						else:
  									lowerCamelCase								=     None
  
  						return tokenizer.pad(
  						    lowerCamelCase__     ,       padding="""longest"""     ,       pad_to_multiple_of=lowerCamelCase__     ,       return_tensors="""pt"""     ,       )
  
  			# Instantiate dataloaders.
  			lowerCamelCase								=     DataLoader(
  			    tokenized_datasets["""train"""]     ,       shuffle=lowerCamelCase__     ,       collate_fn=lowerCamelCase__     ,       batch_size=lowerCamelCase__			)
  			lowerCamelCase								=     DataLoader(
  			    tokenized_datasets["""validation"""]     ,       shuffle=lowerCamelCase__     ,       collate_fn=lowerCamelCase__     ,       batch_size=lowerCamelCase__			)
  
  			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
  
  		UpperCAmelCase      :						Union[str, Any]    =		mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	List[Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,       lowerCamelCase__			) == "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(lowerCamelCase__			)			)
  			def inner_training_loop(lowerCamelCase__		:	List[Any]			):
  						# 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(lowerCamelCase__			)
  
  						# 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=lowerCamelCase__			)
  
  						# 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=lowerCamelCase__			)
  						lowerCamelCase		,		lowerCamelCase								=     get_dataloaders(lowerCamelCase__     ,       lowerCamelCase__			)
  
  						# Instantiate scheduler
  						lowerCamelCase								=     get_linear_schedule_with_warmup(
  						    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__			) * 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		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     accelerator.prepare(
  						    lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
  
  						# Now we train the model
  						for epoch in range(lowerCamelCase__			):
  									model.train()
  									for step, batch in enumerate(lowerCamelCase__			):
  												# And perform gradient accumulation
  												with accelerator.accumulate(lowerCamelCase__			):
  															# We could avoid this line since we set the accelerator with `device_placement=True`.
  															batch.to(accelerator.device			)
  															lowerCamelCase								=     model(**lowerCamelCase__			)
  															lowerCamelCase								=     outputs.loss
  															accelerator.backward(lowerCamelCase__			)
  															optimizer.step()
  															lr_scheduler.step()
  															optimizer.zero_grad()
  
  									model.eval()
  									for step, batch in enumerate(lowerCamelCase__			):
  												# We could avoid this line since we set the accelerator with `device_placement=True`.
  												batch.to(accelerator.device			)
  												with torch.no_grad():
  															lowerCamelCase								=     model(**lowerCamelCase__			)
  												lowerCamelCase								=     outputs.logits.argmax(dim=-1			)
  												lowerCamelCase		,		lowerCamelCase								=     accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
  												metric.add_batch(
  												    predictions=lowerCamelCase__     ,       references=lowerCamelCase__     ,       )
  
  									lowerCamelCase								=     metric.compute()
  									# Use accelerator.print to print only on the main process.
  									accelerator.print(f'epoch {epoch}:'     ,       lowerCamelCase__			)
  
      # 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   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     argparse.ArgumentParser(description="""Simple example of training script."""			)
  			parser.add_argument(
  			    """--mixed_precision"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       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""": 42, """batch_size""": 256}
  			training_function(lowerCamelCase__     ,       lowerCamelCase__			)
  
  
  if __name__ == "__main__":
  		main()
  
  
style_context: 
  
  
  from __future__ import annotations
  
  from collections.abc import Callable
  from typing import Generic, TypeVar
  
  UpperCAmelCase      :						List[str]    =		TypeVar("T")
  UpperCAmelCase      :						Dict    =		TypeVar("U")
  
  
  
  
  
  
  
  class 					__lowercase					(       Generic[T, U]     ):
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self		,			A		,			A  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     key
  					lowerCamelCase								=     val
  					lowerCamelCase								=     0
  					lowerCamelCase								=     None
  					lowerCamelCase								=     None
  
  
  
  
  
  
  
  		def __repr__(   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
  					    self.key		,			self.val		,			self.freq		,			self.next is not None		,			self.prev is not None  )
  
  
  
  
  
  
  
  class 					__lowercase					(       Generic[T, U]     ):
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
  					lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
  					lowerCamelCase		,		lowerCamelCase								=     self.rear, self.head
  
  
  
  
  
  
  
  		def __repr__(   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ["""DoubleLinkedList"""]
  					lowerCamelCase								=     self.head
  					while node.next is not None:
  								rep.append(str(A  )  )
  								lowerCamelCase								=     node.next
  					rep.append(str(self.rear  )  )
  					return ",\n    ".join(A  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.rear.prev
  
  					# All nodes other than self.head are guaranteed to have non-None previous
  					assert previous is not None
  
  					lowerCamelCase								=     node
  					lowerCamelCase								=     previous
  					lowerCamelCase								=     node
  					lowerCamelCase								=     self.rear
  					node.freq += 1
  					self._position_node(A  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					while node.prev is not None and node.prev.freq > node.freq:
  								# swap node with previous node
  								lowerCamelCase								=     node.prev
  
  								lowerCamelCase								=     previous_node.prev
  								lowerCamelCase								=     node.prev
  								lowerCamelCase								=     previous_node
  								lowerCamelCase								=     node
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   DoubleLinkedListNode[T, U] | None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if node.prev is None or node.next is None:
  								return None
  
  					lowerCamelCase								=     node.next
  					lowerCamelCase								=     node.prev
  					lowerCamelCase								=     None
  					lowerCamelCase								=     None
  					return node
  
  
  
  
  
  
  
  class 					__lowercase					(       Generic[T, U]     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				dict[Callable[[T], U], LFUCache[T, U]] 					=		{}
  
  
  
  
  
  
  
  		def __init__(   self		,			A  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     DoubleLinkedList()
  					lowerCamelCase								=     capacity
  					lowerCamelCase								=     0
  					lowerCamelCase								=     0
  					lowerCamelCase								=     0
  					lowerCamelCase								=     {}
  
  
  
  
  
  
  
  		def __repr__(   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return (
  					    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
  					    F'capacity={self.capacity}, current_size={self.num_keys})'
  					)
  
  
  
  
  
  
  
  		def __contains__(   self		,			A  )     ->   bool:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return key in self.cache
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   U | None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if key in self.cache:
  								self.hits += 1
  								lowerCamelCase								=     self.cache[key]
  								lowerCamelCase								=     self.list.remove(self.cache[key]  )
  								assert node == value_node
  
  								# node is guaranteed not None because it is in self.cache
  								assert node is not None
  								self.list.add(A  )
  								return node.val
  					self.miss += 1
  					return None
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A  )     ->   None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if key not in self.cache:
  								if self.num_keys >= self.capacity:
  											# delete first node when over capacity
  											lowerCamelCase								=     self.list.head.next
  
  											# guaranteed to have a non-None first node when num_keys > 0
  											# explain to type checker via assertions
  											assert first_node is not None
  											assert first_node.key is not None
  											assert self.list.remove(A  ) is not None
  											# first_node guaranteed to be in list
  
  											del self.cache[first_node.key]
  											self.num_keys -= 1
  								lowerCamelCase								=     DoubleLinkedListNode(A		,			A  )
  								self.list.add(self.cache[key]  )
  								self.num_keys += 1
  
  					else:
  								lowerCamelCase								=     self.list.remove(self.cache[key]  )
  								assert node is not None  # node guaranteed to be in list
  								lowerCamelCase								=     value
  								self.list.add(A  )
  
  
  
  
  
  
  
  		@classmethod
  		def 				__A      (   cls		,			A = 1_28  )     ->   Callable[[Callable[[T], U]], Callable[..., U]]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					def cache_decorator_inner(A  ) -> Callable[..., U]:
  								def cache_decorator_wrapper(*A  ) -> U:
  											if func not in cls.decorator_function_to_instance_map:
  														lowerCamelCase								=     LFUCache(A  )
  
  											lowerCamelCase								=     cls.decorator_function_to_instance_map[func].get(args[0]  )
  											if result is None:
  														lowerCamelCase								=     func(*A  )
  														cls.decorator_function_to_instance_map[func].put(args[0]		,			A  )
  											return result
  
  								def cache_info() -> LFUCache[T, U]:
  											return cls.decorator_function_to_instance_map[func]
  
  								setattr(A		,			"""cache_info"""		,			A  )  # noqa: B010
  
  								return cache_decorator_wrapper
  
  					return cache_decorator_inner
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  