






'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConditionalDetrImageProcessor






class        a_		(    unittest.TestCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=30    ,	_SCREAMING_SNAKE_CASE=400    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1 / 255    ,	_SCREAMING_SNAKE_CASE=True    ,	)						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      min_resolution
        UpperCamelCase       						=      max_resolution
        UpperCamelCase       						=      do_resize
        UpperCamelCase       						=      size
        UpperCamelCase       						=      do_normalize
        UpperCamelCase       						=      image_mean
        UpperCamelCase       						=      image_std
        UpperCamelCase       						=      do_rescale
        UpperCamelCase       						=      rescale_factor
        UpperCamelCase       						=      do_pad

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False   )						->   Dict:



        """simple docstring"""






        if not batched:
              UpperCamelCase       						=      image_inputs[0]
              if isinstance(_SCREAMING_SNAKE_CASE    ,	Image.Image   ):
                    UpperCamelCase       						=      image.size
              else:
                    UpperCamelCase       						=      image.shape[1], image.shape[2]
              if w < h:
                    UpperCamelCase       						=      int(self.size["""shortest_edge"""] * h / w   )
                    UpperCamelCase       						=      self.size["shortest_edge"]
              elif w > h:
                    UpperCamelCase       						=      self.size["shortest_edge"]
                    UpperCamelCase       						=      int(self.size["""shortest_edge"""] * w / h   )
              else:
                    UpperCamelCase       						=      self.size["shortest_edge"]
                    UpperCamelCase       						=      self.size["shortest_edge"]

        else:
              UpperCamelCase       						=      []
              for image in image_inputs:
                    UpperCamelCase       						=      self.get_expected_values([image]   )
                    expected_values.append((expected_height, expected_width)   )
              UpperCamelCase       						=      max(_SCREAMING_SNAKE_CASE    ,	key=lambda _SCREAMING_SNAKE_CASE   : item[0]   )[0]
              UpperCamelCase       						=      max(_SCREAMING_SNAKE_CASE    ,	key=lambda _SCREAMING_SNAKE_CASE   : item[1]   )[1]

        return expected_height, expected_width






@require_torch
@require_vision
class        a_		(    _UpperCamelCase			, unittest.TestCase       ):
  lowercase  	=   ConditionalDetrImageProcessor if is_vision_available() else None

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      ConditionalDetrImageProcessingTester(self   )

  @property
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""image_mean"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""image_std"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_normalize"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_resize"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""size"""   )   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size    ,	{"""shortest_edge""": 18, """longest_edge""": 1333}   )
        self.assertEqual(image_processor.do_pad    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.image_processing_class.from_dict(
            self.image_processor_dict    ,	size=42    ,	max_size=84    ,	pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE   )
        self.assertEqual(image_processor.size    ,	{"""shortest_edge""": 42, """longest_edge""": 84}   )
        self.assertEqual(image_processor.do_pad    ,	_SCREAMING_SNAKE_CASE   )

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



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	Image.Image   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	return_tensors="""pt"""   ).pixel_values

        UpperCamelCase       						=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   )

        self.assertEqual(
            encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

        # Test batched
        UpperCamelCase       						=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE    ,	batched=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,	)

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	numpify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	np.ndarray   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	return_tensors="""pt"""   ).pixel_values

        UpperCamelCase       						=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   )

        self.assertEqual(
            encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values

        UpperCamelCase       						=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE    ,	batched=_SCREAMING_SNAKE_CASE   )

        self.assertEqual(
            encoded_images.shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,	)

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	torchify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	torch.Tensor   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	return_tensors="""pt"""   ).pixel_values

        UpperCamelCase       						=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE   )

        self.assertEqual(
            encoded_images.shape    ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values

        UpperCamelCase       						=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE    ,	batched=_SCREAMING_SNAKE_CASE   )

        self.assertEqual(
            encoded_images.shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,	)

  @slow
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,	"""r"""   ) as f:
              UpperCamelCase       						=      json.loads(f.read()   )

        UpperCamelCase       						=      {"image_id": 39769, "annotations": target}

        # encode them
        UpperCamelCase       						=      ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""   )
        UpperCamelCase       						=      image_processing(images=_SCREAMING_SNAKE_CASE    ,	annotations=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )

        # verify pixel values
        UpperCamelCase       						=      torch.Size([1, 3, 800, 1066]   )
        self.assertEqual(encoding["""pixel_values"""].shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]   )
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )

        # verify area
        UpperCamelCase       						=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify boxes
        UpperCamelCase       						=      torch.Size([6, 4]   )
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-3   )   )
        # verify image_id
        UpperCamelCase       						=      torch.tensor([39769]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify is_crowd
        UpperCamelCase       						=      torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify class_labels
        UpperCamelCase       						=      torch.tensor([75, 75, 63, 65, 17, 17]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify orig_size
        UpperCamelCase       						=      torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify size
        UpperCamelCase       						=      torch.tensor([800, 1066]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,	_SCREAMING_SNAKE_CASE   )   )


  @slow
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,	"""r"""   ) as f:
              UpperCamelCase       						=      json.loads(f.read()   )

        UpperCamelCase       						=      {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

        UpperCamelCase       						=      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

        # encode them
        UpperCamelCase       						=      ConditionalDetrImageProcessor(format="""coco_panoptic"""   )
        UpperCamelCase       						=      image_processing(images=_SCREAMING_SNAKE_CASE    ,	annotations=_SCREAMING_SNAKE_CASE    ,	masks_path=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   )

        # verify pixel values
        UpperCamelCase       						=      torch.Size([1, 3, 800, 1066]   )
        self.assertEqual(encoding["""pixel_values"""].shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]   )
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )

        # verify area
        UpperCamelCase       						=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify boxes
        UpperCamelCase       						=      torch.Size([6, 4]   )
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-3   )   )
        # verify image_id
        UpperCamelCase       						=      torch.tensor([39769]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify is_crowd
        UpperCamelCase       						=      torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify class_labels
        UpperCamelCase       						=      torch.tensor([17, 17, 63, 75, 75, 93]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify masks
        UpperCamelCase       						=      822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,	_SCREAMING_SNAKE_CASE   )
        # verify orig_size
        UpperCamelCase       						=      torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,	_SCREAMING_SNAKE_CASE   )   )
        # verify size
        UpperCamelCase       						=      torch.tensor([800, 1066]   )
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,	_SCREAMING_SNAKE_CASE   )   )








'''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())))
