


"""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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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						:				int             =      16
UpperCamelCase						:				Optional[int]             =      32







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 16	)      ->		List[str]:







		"""simple docstring"""





		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

	UpperCamelCase						:				Union[str, Any]             =      mocked_dataloaders  # noqa: F811







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		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']	)

		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

		set_seed(UpperCamelCase__	)
		A       , A					       = get_dataloaders(UpperCamelCase__ ,			UpperCamelCase__	)
		# 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__	)

		# Now we train the model
		for epoch in range(UpperCamelCase__	):
				model.train()
				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()

				model.eval()
				A					       = 0
				for step, batch in enumerate(UpperCamelCase__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								A					       = model(**UpperCamelCase__	)
						A					       = outputs.logits.argmax(dim=-1	)
						A       , A					       = accelerator.gather((predictions, batch['labels'])	)
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(UpperCamelCase__	) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										A					       = predictions[: len(eval_dataloader.dataset	) - samples_seen]
										A					       = references[: len(eval_dataloader.dataset	) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # 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__	)







def  __snake_case       (   )      ->		str:







		"""simple docstring"""





		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.'	)
		A					       = parser.parse_args()
		A					       = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	main()









"""simple docstring"""


from math import sqrt







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = 0
		for i in range(1 ,			int(sqrt(UpperCamelCase__	) + 1	)	):
				if n % i == 0 and i != sqrt(UpperCamelCase__	):
						total += i + n // i
				elif i == sqrt(UpperCamelCase__	):
						total += i
		return total - n







def  __snake_case       (   UpperCamelCase__ = 10000	)      ->		int:







		"""simple docstring"""





		A					       = sum(
		    i
		    for i in range(1 ,			UpperCamelCase__	)
		    if sum_of_divisors(sum_of_divisors(UpperCamelCase__	)	) == i and sum_of_divisors(UpperCamelCase__	) != i	)
		return total


if __name__ == "__main__":
	print(solution(int(str(input()).strip())))





