code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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
  #
  ########################################################################
  
  _a      :				Union[str, Any]											=					1_6
  _a      :				int											=					3_2
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase = 16			)      ->      int:
  			__lowerCAmelCase      		=       AutoTokenizer.from_pretrained("""bert-base-cased"""			)
  			__lowerCAmelCase      		=       load_dataset("""glue"""      ,			"""mrpc"""			)
  
  			def tokenize_function(lowercase			):
  						# max_length=None => use the model max length (it's actually the default)
  						__lowerCAmelCase      		=       tokenizer(examples["""sentence1"""]      ,			examples["""sentence2"""]      ,			truncation=lowercase      ,			max_length=lowercase			)
  						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(
  						    lowercase      ,			batched=lowercase      ,			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(lowercase			):
  						# On TPU it's best to pad everything to the same length or training will be very slow.
  						__lowerCAmelCase      		=       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":
  									__lowerCAmelCase      		=       16
  						elif accelerator.mixed_precision != "no":
  									__lowerCAmelCase      		=       8
  						else:
  									__lowerCAmelCase      		=       None
  
  						return tokenizer.pad(
  						    lowercase      ,			padding="""longest"""      ,			max_length=lowercase      ,			pad_to_multiple_of=lowercase      ,			return_tensors="""pt"""      ,			)
  
  			# Instantiate dataloaders.
  			__lowerCAmelCase      		=       DataLoader(
  			    tokenized_datasets["""train"""]      ,			shuffle=lowercase      ,			collate_fn=lowercase      ,			batch_size=lowercase			)
  			__lowerCAmelCase      		=       DataLoader(
  			    tokenized_datasets["""validation"""]      ,			shuffle=lowercase      ,			collate_fn=lowercase      ,			batch_size=lowercase			)
  
  			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
  
  	_a      :				Any											=					mocked_dataloaders  # noqa: F811
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Dict:
  			# For testing only
  			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,			lowercase			) == "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"""]			)
  
  			# 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":
  									__lowerCAmelCase      		=       args.checkpointing_steps
  						elif args.checkpointing_steps.isdigit():
  									__lowerCAmelCase      		=       int(args.checkpointing_steps			)
  						else:
  									raise ValueError(
  									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
  			else:
  						__lowerCAmelCase      		=       None
  
  			set_seed(lowercase			)
  
  			__lowerCAmelCase		, __lowerCAmelCase      		=       get_dataloaders(lowercase      ,			lowercase			)
  			__lowerCAmelCase      		=       evaluate.load("""glue"""      ,			"""mrpc"""			)
  
  			# If the batch size is too big we use gradient accumulation
  			__lowerCAmelCase      		=       1
  			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  						__lowerCAmelCase      		=       batch_size // MAX_GPU_BATCH_SIZE
  						__lowerCAmelCase      		=       MAX_GPU_BATCH_SIZE
  
  			# 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=lowercase			)
  
  			# 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=lowercase			)
  
  			# Instantiate scheduler
  			__lowerCAmelCase      		=       get_linear_schedule_with_warmup(
  			    optimizer=lowercase      ,			num_warmup_steps=100      ,			num_training_steps=(len(lowercase			) * 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.
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       accelerator.prepare(
  			    lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
  
  			# New Code #
  			# We need to keep track of how many total steps we have iterated over
  			__lowerCAmelCase      		=       0
  			# We also need to keep track of the stating epoch so files are named properly
  			__lowerCAmelCase      		=       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			)
  									__lowerCAmelCase      		=       os.path.basename(args.resume_from_checkpoint			)
  						else:
  									# Get the most recent checkpoint
  									__lowerCAmelCase      		=       [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
  									dirs.sort(key=os.path.getctime			)
  									__lowerCAmelCase      		=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  						# Extract `epoch_{i}` or `step_{i}`
  						__lowerCAmelCase      		=       os.path.splitext(lowercase			)[0]
  
  						if "epoch" in training_difference:
  									__lowerCAmelCase      		=       int(training_difference.replace("""epoch_"""      ,			""""""			)			) + 1
  									__lowerCAmelCase      		=       None
  						else:
  									__lowerCAmelCase      		=       int(training_difference.replace("""step_"""      ,			""""""			)			)
  									__lowerCAmelCase      		=       resume_step // len(lowercase			)
  									resume_step -= starting_epoch * len(lowercase			)
  
      # Now we train the model
  			for epoch in range(lowercase      ,			lowercase			):
  						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
  									__lowerCAmelCase      		=       accelerator.skip_first_batches(lowercase      ,			lowercase			)
  									overall_step += resume_step
  						else:
  									# After the first iteration though, we need to go back to the original dataloader
  									__lowerCAmelCase      		=       train_dataloader
  						for step, batch in enumerate(lowercase			):
  									# We could avoid this line since we set the accelerator with `device_placement=True`.
  									batch.to(accelerator.device			)
  									__lowerCAmelCase      		=       model(**lowercase			)
  									__lowerCAmelCase      		=       outputs.loss
  									__lowerCAmelCase      		=       loss / gradient_accumulation_steps
  									accelerator.backward(lowercase			)
  									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(lowercase      ,			lowercase			):
  												__lowerCAmelCase      		=       f'step_{overall_step}'
  												if overall_step % checkpointing_steps == 0:
  															if args.output_dir is not None:
  																		__lowerCAmelCase      		=       os.path.join(args.output_dir      ,			lowercase			)
  															accelerator.save_state(lowercase			)
  
  						model.eval()
  						for step, batch in enumerate(lowercase			):
  									# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
  									batch.to(accelerator.device			)
  									with torch.no_grad():
  												__lowerCAmelCase      		=       model(**lowercase			)
  									__lowerCAmelCase      		=       outputs.logits.argmax(dim=-1			)
  									__lowerCAmelCase		, __lowerCAmelCase      		=       accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
  									metric.add_batch(
  									    predictions=lowercase      ,			references=lowercase      ,			)
  
  						__lowerCAmelCase      		=       metric.compute()
  						# Use accelerator.print to print only on the main process.
  						accelerator.print(f'epoch {epoch}:'      ,			lowercase			)
  
  						# 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":
  									__lowerCAmelCase      		=       f'epoch_{epoch}'
  									if args.output_dir is not None:
  												__lowerCAmelCase      		=       os.path.join(args.output_dir      ,			lowercase			)
  									accelerator.save_state(lowercase			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      List[str]:
  			__lowerCAmelCase      		=       argparse.ArgumentParser(description="""Simple example of training script."""			)
  			parser.add_argument(
  			    """--mixed_precision"""      ,			type=lowercase      ,			default=lowercase      ,			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=lowercase      ,			default=lowercase      ,			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=lowercase      ,			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=lowercase      ,			default=lowercase      ,			help="""If the training should continue from a checkpoint folder."""      ,			)
  			__lowerCAmelCase      		=       parser.parse_args()
  			__lowerCAmelCase      		=       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  			training_function(lowercase      ,			lowercase			)
  
  
  if __name__ == "__main__":
  	main()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       3
  			__lowerCAmelCase      		=       5
  			__lowerCAmelCase      		=       1
  			while True:
  						__lowerCAmelCase      		=       z * temp
  						if result < n:
  									zmulti.append(lowercase			)
  									temp += 1
  						else:
  									__lowerCAmelCase      		=       1
  									break
  			while True:
  						__lowerCAmelCase      		=       x * temp
  						if result < n:
  									xmulti.append(lowercase			)
  									temp += 1
  						else:
  									break
  			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
  			return sum(lowercase			)
  
  
  if __name__ == "__main__":
  	print(f'{solution() = }')
  
  