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 experiment tracking 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
#
########################################################################

lowerCamelCase_    :		Dict							=	16
lowerCamelCase_    :		Union[str, Any]							=	32
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase = 16						):
					__a		 =       AutoTokenizer.from_pretrained('bert-base-cased'						)
					__a		 =       load_dataset('glue'				,    'mrpc'						)

					def tokenize_function(__lowerCamelCase						):
										# max_length=None => use the model max length (it's actually the default)
										__a		 =       tokenizer(examples['sentence1']				,    examples['sentence2']				,    truncation=__lowerCamelCase				,    max_length=__lowerCamelCase						)
										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(
										    __lowerCamelCase				,    batched=__lowerCamelCase				,    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(__lowerCamelCase						):
										# 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(
										    __lowerCamelCase				,    padding='longest'				,    max_length=__lowerCamelCase				,    pad_to_multiple_of=__lowerCamelCase				,    return_tensors='pt'				,    )

					# Instantiate dataloaders.
					__a		 =       DataLoader(
					    tokenized_datasets['train']				,    shuffle=__lowerCamelCase				,    collate_fn=__lowerCamelCase				,    batch_size=__lowerCamelCase						)
					__a		 =       DataLoader(
					    tokenized_datasets['validation']				,    shuffle=__lowerCamelCase				,    collate_fn=__lowerCamelCase				,    batch_size=__lowerCamelCase						)

					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

	lowerCamelCase_    :		List[Any]							=	mocked_dataloaders  # noqa: F811
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS'				,    __lowerCamelCase						) == "1":
										__a		 =       2
					# Initialize Accelerator

					# New Code #
					# We pass in "all" to `log_with` to grab all available trackers in the environment
					# Note: If using a custom `Tracker` class, should be passed in here such as:
					# >>> log_with = ["all", MyCustomTrackerClassInstance()]
					if args.with_tracking:
										__a		 =       Accelerator(
										    cpu=args.cpu				,    mixed_precision=args.mixed_precision				,    log_with='all'				,    project_dir=args.project_dir						)
					else:
										__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']						)
					set_seed(__lowerCamelCase						)

					__a ,				__a		 =       get_dataloaders(__lowerCamelCase				,    __lowerCamelCase						)
					__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=__lowerCamelCase						)

					# 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=__lowerCamelCase						)

					# Instantiate scheduler
					__a		 =       get_linear_schedule_with_warmup(
					    optimizer=__lowerCamelCase				,    num_warmup_steps=100				,    num_training_steps=(len(__lowerCamelCase						) * 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(
					    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)

					# New Code #
					# We need to initialize the trackers we use. Overall configurations can also be stored
					if args.with_tracking:
										__a		 =       os.path.split(__lowerCamelCase						)[-1].split('.'						)[0]
										accelerator.init_trackers(__lowerCamelCase				,    __lowerCamelCase						)

					# Now we train the model
					for epoch in range(__lowerCamelCase						):
										model.train()
										# New Code #
										# For our tracking example, we will log the total loss of each epoch
										if args.with_tracking:
															__a		 =       0
										for step, batch in enumerate(__lowerCamelCase						):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device						)
															__a		 =       model(**__lowerCamelCase						)
															__a		 =       outputs.loss
															# New Code #
															if args.with_tracking:
																				total_loss += loss.detach().float()
															__a		 =       loss / gradient_accumulation_steps
															accelerator.backward(__lowerCamelCase						)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										for step, batch in enumerate(__lowerCamelCase						):
															# 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(**__lowerCamelCase						)
															__a		 =       outputs.logits.argmax(dim=-1						)
															__a ,				__a		 =       accelerator.gather_for_metrics((predictions, batch['labels'])						)
															metric.add_batch(
															    predictions=__lowerCamelCase				,    references=__lowerCamelCase				,    )

										__a		 =       metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:'''				,    __lowerCamelCase						)

										# New Code #
										# To actually log, we call `Accelerator.log`
										# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
										if args.with_tracking:
															accelerator.log(
															    {
															        'accuracy': eval_metric['accuracy'],
															        'f1': eval_metric['f1'],
															        'train_loss': total_loss.item() / len(__lowerCamelCase						),
															        'epoch': epoch,
															    }				,    step=__lowerCamelCase				,    )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
					if args.with_tracking:
										accelerator.end_training()
def 							lowerCAmelCase(     ):
					__a		 =       argparse.ArgumentParser(description='Simple example of training script.'						)
					parser.add_argument(
					    '--mixed_precision'				,    type=__lowerCamelCase				,    default=__lowerCamelCase				,    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(
					    '--with_tracking'				,    action='store_true'				,    help='Whether to load in all available experiment trackers from the environment and use them for logging.'				,    )
					parser.add_argument(
					    '--project_dir'				,    type=__lowerCamelCase				,    default='logs'				,    help='Location on where to store experiment tracking logs` and relevent project information'				,    )
					__a		 =       parser.parse_args()
					__a		 =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(__lowerCamelCase				,    __lowerCamelCase						)


if __name__ == "__main__":
	main()





from math import sqrt
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       0
					for i in range(1				,    int(sqrt(__lowerCamelCase						) + 1						)						):
										if n % i == 0 and i != sqrt(__lowerCamelCase						):
															total += i + n // i
										elif i == sqrt(__lowerCamelCase						):
															total += i
					return total - n
def 							lowerCAmelCase(     __lowerCamelCase = 1_0000						):
					__a		 =       sum(
					    i
					    for i in range(1				,    __lowerCamelCase						)
					    if sum_of_divisors(sum_of_divisors(__lowerCamelCase						)						) == i and sum_of_divisors(__lowerCamelCase						) != i						)
					return total


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




