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,
  # specifically showcasing the checkpointing capability,
  # and builds off the `nlp_example.py` script.
  #
  # 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)
  #
  # To help focus on the differences in the code, building `DataLoaders`
  # was refactored into its own function.
  # 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_								=    16
  lowercase_								=    32
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase = 16				)    ->  Optional[int]:
  						_a     =			AutoTokenizer.from_pretrained('bert-base-cased'				)
  						_a     =			load_dataset('glue'							,     'mrpc'				)
  
  						def tokenize_function(_UpperCAmelCase				):
  												# max_length=None => use the model max length (it's actually the default)
  												_a     =			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():
  												_a     =			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
  						_a     =			tokenized_datasets.rename_column('label'							,     'labels'				)
  
  						def collate_fn(_UpperCAmelCase				):
  												# On TPU it's best to pad everything to the same length or training will be very slow.
  												_a     =			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":
  																		_a     =			16
  												elif accelerator.mixed_precision != "no":
  																		_a     =			8
  												else:
  																		_a     =			None
  
  												return tokenizer.pad(
  												    _UpperCAmelCase							,     padding='longest'							,     max_length=_UpperCAmelCase							,     pad_to_multiple_of=_UpperCAmelCase							,     return_tensors='pt'							,     )
  
  						# Instantiate dataloaders.
  						_a     =			DataLoader(
  						    tokenized_datasets['train']							,     shuffle=_UpperCAmelCase							,     collate_fn=_UpperCAmelCase							,     batch_size=_UpperCAmelCase				)
  						_a     =			DataLoader(
  						    tokenized_datasets['validation']							,     shuffle=_UpperCAmelCase							,     collate_fn=_UpperCAmelCase							,     batch_size=_UpperCAmelCase				)
  
  						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_								=    mocked_dataloaders  # noqa: F811
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
  						# For testing only
  						if os.environ.get('TESTING_MOCKED_DATALOADERS'							,     _UpperCAmelCase				) == "1":
  												_a     =			2
  						# Initialize accelerator
  						_a     =			Accelerator(cpu=args.cpu							,     mixed_precision=args.mixed_precision				)
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						_a     =			config['lr']
  						_a     =			int(config['num_epochs']				)
  						_a     =			int(config['seed']				)
  						_a     =			int(config['batch_size']				)
  
  						# New Code #
  						# Parse out whether we are saving every epoch or after a certain number of batches
  						if hasattr(args.checkpointing_steps							,     'isdigit'				):
  												if args.checkpointing_steps == "epoch":
  																		_a     =			args.checkpointing_steps
  												elif args.checkpointing_steps.isdigit():
  																		_a     =			int(args.checkpointing_steps				)
  												else:
  																		raise ValueError(
  																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""				)
  						else:
  												_a     =			None
  
  						set_seed(_UpperCAmelCase				)
  
  						_a ,	_a     =			get_dataloaders(_UpperCAmelCase							,     _UpperCAmelCase				)
  						_a     =			evaluate.load('glue'							,     'mrpc'				)
  
  						# If the batch size is too big we use gradient accumulation
  						_a     =			1
  						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  												_a     =			batch_size // MAX_GPU_BATCH_SIZE
  												_a     =			MAX_GPU_BATCH_SIZE
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						_a     =			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							,     return_dict=_UpperCAmelCase				)
  
  						# 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).
  						_a     =			model.to(accelerator.device				)
  
  						# Instantiate optimizer
  						_a     =			AdamW(params=model.parameters()							,     lr=_UpperCAmelCase				)
  
  						# Instantiate scheduler
  						_a     =			get_linear_schedule_with_warmup(
  						    optimizer=_UpperCAmelCase							,     num_warmup_steps=100							,     num_training_steps=(len(_UpperCAmelCase				) * 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.
  						_a ,	_a ,	_a ,	_a ,	_a     =			accelerator.prepare(
  						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  
  						# New Code #
  						# We need to keep track of how many total steps we have iterated over
  						_a     =			0
  						# We also need to keep track of the stating epoch so files are named properly
  						_a     =			0
  
  						# We need to load the checkpoint back in before training here with `load_state`
  						# The total number of epochs is adjusted based on where the state is being loaded from,
  						# as we assume continuation of the same training script
  						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				)
  																		_a     =			os.path.basename(args.resume_from_checkpoint				)
  												else:
  																		# Get the most recent checkpoint
  																		_a     =			[f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
  																		dirs.sort(key=os.path.getctime				)
  																		_a     =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  												# Extract `epoch_{i}` or `step_{i}`
  												_a     =			os.path.splitext(_UpperCAmelCase				)[0]
  
  												if "epoch" in training_difference:
  																		_a     =			int(training_difference.replace('epoch_'							,     ''				)				) + 1
  																		_a     =			None
  												else:
  																		_a     =			int(training_difference.replace('step_'							,     ''				)				)
  																		_a     =			resume_step // len(_UpperCAmelCase				)
  																		resume_step -= starting_epoch * len(_UpperCAmelCase				)
  
      # Now we train the model
  						for epoch in range(_UpperCAmelCase							,     _UpperCAmelCase				):
  												model.train()
  												# New Code #
  												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
  																		_a     =			accelerator.skip_first_batches(_UpperCAmelCase							,     _UpperCAmelCase				)
  																		overall_step += resume_step
  												else:
  																		# After the first iteration though, we need to go back to the original dataloader
  																		_a     =			train_dataloader
  												for step, batch in enumerate(_UpperCAmelCase				):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		batch.to(accelerator.device				)
  																		_a     =			model(**_UpperCAmelCase				)
  																		_a     =			outputs.loss
  																		_a     =			loss / gradient_accumulation_steps
  																		accelerator.backward(_UpperCAmelCase				)
  																		if step % gradient_accumulation_steps == 0:
  																								optimizer.step()
  																								lr_scheduler.step()
  																								optimizer.zero_grad()
  																		# New Code #
  																		overall_step += 1
  
  																		# New Code #
  																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  																		# These are saved to folders named `step_{overall_step}`
  																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  																		# If mixed precision was used, will also save a "scalar.bin" file
  																		if isinstance(_UpperCAmelCase							,     _UpperCAmelCase				):
  																								_a     =			f"""step_{overall_step}"""
  																								if overall_step % checkpointing_steps == 0:
  																														if args.output_dir is not None:
  																																				_a     =			os.path.join(args.output_dir							,     _UpperCAmelCase				)
  																														accelerator.save_state(_UpperCAmelCase				)
  
  												model.eval()
  												for step, batch in enumerate(_UpperCAmelCase				):
  																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
  																		batch.to(accelerator.device				)
  																		with torch.no_grad():
  																								_a     =			model(**_UpperCAmelCase				)
  																		_a     =			outputs.logits.argmax(dim=-1				)
  																		_a ,	_a     =			accelerator.gather_for_metrics((predictions, batch['labels'])				)
  																		metric.add_batch(
  																		    predictions=_UpperCAmelCase							,     references=_UpperCAmelCase							,     )
  
  												_a     =			metric.compute()
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(f"""epoch {epoch}:"""							,     _UpperCAmelCase				)
  
  												# New Code #
  												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
  												# These are saved to folders named `epoch_{epoch}`
  												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
  												# If mixed precision was used, will also save a "scalar.bin" file
  												if checkpointing_steps == "epoch":
  																		_a     =			f"""epoch_{epoch}"""
  																		if args.output_dir is not None:
  																								_a     =			os.path.join(args.output_dir							,     _UpperCAmelCase				)
  																		accelerator.save_state(_UpperCAmelCase				)
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  List[Any]:
  						_a     =			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(
  						    '--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(
  						    '--resume_from_checkpoint'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='If the training should continue from a checkpoint folder.'							,     )
  						_a     =			parser.parse_args()
  						_a     =			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  						training_function(_UpperCAmelCase							,     _UpperCAmelCase				)
  
  
  if __name__ == "__main__":
  		main()
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  