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