






'''simple docstring'''

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__											=     logging.getLogger()







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      argparse.ArgumentParser()
      parser.add_argument("""-f"""					)
      UpperCamelCase       						=      parser.parse_args()
      return args.f







def 					lowercase__				(   __UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      {}
      UpperCamelCase       						=      os.path.join(__UpperCamelCase	,		"""all_results.json"""					)
      if os.path.exists(__UpperCamelCase					):
            with open(__UpperCamelCase	,		"""r"""					) as f:
                  UpperCamelCase       						=      json.load(__UpperCamelCase					)
      else:
            raise ValueError(F"can't find {path}"					)
      return results







def 					lowercase__				(   )->				int:
      UpperCamelCase       						=      torch.cuda.is_available() and torch_device == """cuda"""
      return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE__											=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class        a_		(    lowerCamelCase       ):

  @classmethod
  def 						A__		(   cls   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      tempfile.mkdtemp()
        UpperCamelCase       						=      os.path.join(cls.tmpdir    ,	"""default_config.yml"""   )
        write_basic_config(save_location=cls.configPath   )
        UpperCamelCase       						=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]

  @classmethod
  def 						A__		(   cls   )						->   Optional[Any]:



        """simple docstring"""






        shutil.rmtree(cls.tmpdir   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        if is_cuda_and_apex_available():
              testargs.append("""--fp16"""   )

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.7_5   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""glue_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        if torch.cuda.device_count() > 1:
              # Skipping because there are not enough batches to train the model + would need a drop_last to work.
              return

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertLess(result["""perplexity"""]    ,	100   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""clm_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertLess(result["""perplexity"""]    ,	42   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""mlm_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      7 if get_gpu_count() > 1 else 2

        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.7_5   )
        self.assertLess(result["""train_loss"""]    ,	0.5   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""ner_no_trainer"""   )   )   )

  @unittest.skip(reason="""Fix me @muellerzr"""   )
  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result["""eval_f1"""]    ,	28   )
        self.assertGreaterEqual(result["""eval_exact"""]    ,	28   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""qa_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.8   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""swag_no_trainer"""   )   )   )

  @slow
  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_rouge1"""]    ,	10   )
        self.assertGreaterEqual(result["""eval_rouge2"""]    ,	2   )
        self.assertGreaterEqual(result["""eval_rougeL"""]    ,	7   )
        self.assertGreaterEqual(result["""eval_rougeLsum"""]    ,	7   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""summarization_no_trainer"""   )   )   )

  @slow
  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_bleu"""]    ,	30   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""translation_no_trainer"""   )   )   )

  @slow
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      logging.StreamHandler(sys.stdout   )
        logger.addHandler(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_overall_accuracy"""]    ,	0.1_0   )


  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

        if is_cuda_and_apex_available():
              testargs.append("""--fp16"""   )

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        # The base model scores a 25%
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.6   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""step_1"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""image_classification_no_trainer"""   )   )   )








'''simple docstring'''

from math import sqrt







def 					lowercase__				(   __UpperCamelCase					)->				int:
      UpperCamelCase       						=      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 					lowercase__				(   __UpperCamelCase = 10000					)->				int:
      UpperCamelCase       						=      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())))
