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
  #
  ########################################################################
  
  UpperCamelCase_	:    List[Any]  					=       16
  UpperCamelCase_	:    Optional[int]  					=       32
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Accelerator   , _UpperCAmelCase     :      int = 16     )      -> List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Any    			=      AutoTokenizer.from_pretrained("bert-base-cased"     )
  						_lowercase					:  Dict    			=      load_dataset("glue"   , "mrpc"     )
  
  						def tokenize_function(_UpperCAmelCase     :      Tuple     ):
  												# max_length=None => use the model max length (it's actually the default)
  												_lowercase					:  Optional[int]    			=      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():
  												_lowercase					:  Tuple    			=      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
  						_lowercase					:  List[str]    			=      tokenized_datasets.rename_column("label"   , "labels"     )
  
  						def collate_fn(_UpperCAmelCase     :      Dict     ):
  												# On TPU it's best to pad everything to the same length or training will be very slow.
  												_lowercase					:  int    			=      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":
  																		_lowercase					:  Union[str, Any]    			=      16
  												elif accelerator.mixed_precision != "no":
  																		_lowercase					:  List[Any]    			=      8
  												else:
  																		_lowercase					:  List[Any]    			=      None
  
  												return tokenizer.pad(
  												    _UpperCAmelCase   , padding="longest"   , max_length=_UpperCAmelCase   , pad_to_multiple_of=_UpperCAmelCase   , return_tensors="pt"   , )
  
  						# Instantiate dataloaders.
  						_lowercase					:  List[str]    			=      DataLoader(
  						    tokenized_datasets["train"]   , shuffle=_UpperCAmelCase   , collate_fn=_UpperCAmelCase   , batch_size=_UpperCAmelCase     )
  						_lowercase					:  Any    			=      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
  
  							UpperCamelCase_	:    Union[str, Any]  					=       mocked_dataloaders  # noqa: F811
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      Dict     )      -> str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if os.environ.get("TESTING_MOCKED_DATALOADERS"   , _UpperCAmelCase     ) == "1":
  												_lowercase					:  Union[str, Any]    			=      2
  						# Initialize accelerator
  						_lowercase					:  Union[str, Any]    			=      Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision     )
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						_lowercase					:  Optional[int]    			=      config["lr"]
  						_lowercase					:  Optional[int]    			=      int(config["num_epochs"]     )
  						_lowercase					:  List[str]    			=      int(config["seed"]     )
  						_lowercase					:  Any    			=      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":
  																		_lowercase					:  Optional[Any]    			=      args.checkpointing_steps
  												elif args.checkpointing_steps.isdigit():
  																		_lowercase					:  Dict    			=      int(args.checkpointing_steps     )
  												else:
  																		raise ValueError(
  																		    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
  						else:
  												_lowercase					:  List[Any]    			=      None
  
  						set_seed(_UpperCAmelCase     )
  
  						_lowercase					:  Union[str, Any]    			=      get_dataloaders(_UpperCAmelCase   , _UpperCAmelCase     )
  						_lowercase					:  str    			=      evaluate.load("glue"   , "mrpc"     )
  
  						# If the batch size is too big we use gradient accumulation
  						_lowercase					:  Union[str, Any]    			=      1
  						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  												_lowercase					:  Any    			=      batch_size // MAX_GPU_BATCH_SIZE
  												_lowercase					:  Tuple    			=      MAX_GPU_BATCH_SIZE
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						_lowercase					:  Optional[Any]    			=      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).
  						_lowercase					:  List[Any]    			=      model.to(accelerator.device     )
  
  						# Instantiate optimizer
  						_lowercase					:  Any    			=      AdamW(params=model.parameters()   , lr=_UpperCAmelCase     )
  
  						# Instantiate scheduler
  						_lowercase					:  List[str]    			=      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.
  						_lowercase					:  Optional[Any]    			=      accelerator.prepare(
  						    _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase     )
  
  						# New Code #
  						# We need to keep track of how many total steps we have iterated over
  						_lowercase					:  Tuple    			=      0
  						# We also need to keep track of the stating epoch so files are named properly
  						_lowercase					:  str    			=      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     )
  																		_lowercase					:  int    			=      os.path.basename(args.resume_from_checkpoint     )
  												else:
  																		# Get the most recent checkpoint
  																		_lowercase					:  Optional[int]    			=      [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
  																		dirs.sort(key=os.path.getctime     )
  																		_lowercase					:  int    			=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  												# Extract `epoch_{i}` or `step_{i}`
  												_lowercase					:  List[Any]    			=      os.path.splitext(_UpperCAmelCase     )[0]
  
  												if "epoch" in training_difference:
  																		_lowercase					:  Dict    			=      int(training_difference.replace("epoch_"   , ""     )     ) + 1
  																		_lowercase					:  Tuple    			=      None
  												else:
  																		_lowercase					:  List[str]    			=      int(training_difference.replace("step_"   , ""     )     )
  																		_lowercase					:  List[str]    			=      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
  																		_lowercase					:  Any    			=      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
  																		_lowercase					:  Tuple    			=      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     )
  																		_lowercase					:  Any    			=      model(**_UpperCAmelCase     )
  																		_lowercase					:  Dict    			=      outputs.loss
  																		_lowercase					:  Optional[Any]    			=      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     ):
  																								_lowercase					:  Dict    			=      f"""step_{overall_step}"""
  																								if overall_step % checkpointing_steps == 0:
  																														if args.output_dir is not None:
  																																				_lowercase					:  List[str]    			=      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():
  																								_lowercase					:  Union[str, Any]    			=      model(**_UpperCAmelCase     )
  																		_lowercase					:  Tuple    			=      outputs.logits.argmax(dim=-1     )
  																		_lowercase					:  Optional[Any]    			=      accelerator.gather_for_metrics((predictions, batch["labels"])     )
  																		metric.add_batch(
  																		    predictions=_UpperCAmelCase   , references=_UpperCAmelCase   , )
  
  												_lowercase					:  str    			=      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":
  																		_lowercase					:  List[str]    			=      f"""epoch_{epoch}"""
  																		if args.output_dir is not None:
  																								_lowercase					:  List[str]    			=      os.path.join(args.output_dir   , _UpperCAmelCase     )
  																		accelerator.save_state(_UpperCAmelCase     )
  
  
  
  def 		UpperCamelCase    (					)      -> Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Union[str, Any]    			=      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."   , )
  						_lowercase					:  Dict    			=      parser.parse_args()
  						_lowercase					:  str    			=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  						training_function(_UpperCAmelCase   , _UpperCAmelCase     )
  
  
  if __name__ == "__main__":
  							main()
  
  
  
  
  
  
  
style_context: 
  import os
  
  
  
  def 		UpperCamelCase    (					)      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Tuple    			=      0
  						_lowercase					:  List[Any]    			=      0
  						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
  												_lowercase					:  Any    			=      str(file.readlines()[0]     )
  												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )
  
  						name.sort()
  						for i in range(len(_UpperCAmelCase     )     ):
  												for j in name[i]:
  																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
  												total_sum += (i + 1) * temp_sum
  												_lowercase					:  Tuple    			=      0
  						return total_sum
  
  
  if __name__ == "__main__":
  							print(solution())
  
  
  
  
  