


'''simple docstring'''

def __snake_case					(      UpperCAmelCase_						:						int = 1000		):
 lowerCamelCase_      =  2**power
 lowerCamelCase_      =  0
 while n:
  lowerCamelCase_ ,lowerCamelCase_      =  r + n % 10, n // 10
 return r


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






'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class 						lowerCAmelCase__   :



   """simple docstring"""



   @staticmethod
   def       UpperCAmelCase__		(		*__SCREAMING_SNAKE_CASE					:       Tuple					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> List[str]:


        """simple docstring"""




        pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      MODEL_FOR_OBJECT_DETECTION_MAPPING
 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					,							threshold=0.0			)

      self.assertGreater(len(__SCREAMING_SNAKE_CASE			)					,							0			)
      for detected_object in outputs:
           self.assertEqual(
               __SCREAMING_SNAKE_CASE					,							{
                   """score""": ANY(__SCREAMING_SNAKE_CASE			),
                   """label""": ANY(__SCREAMING_SNAKE_CASE			),
                   """box""": {"""xmin""": ANY(__SCREAMING_SNAKE_CASE			), """ymin""": ANY(__SCREAMING_SNAKE_CASE			), """xmax""": ANY(__SCREAMING_SNAKE_CASE			), """ymax""": ANY(__SCREAMING_SNAKE_CASE			)},
               }					,							)

      import datasets

      __SCREAMING_SNAKE_CASE     =      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""					,							"""image"""					,							split="""test"""			)

      __SCREAMING_SNAKE_CASE     =      [
          Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			),
          """http://images.cocodataset.org/val2017/000000039769.jpg""",
          # RGBA
          dataset[0]["""file"""],
          # LA
          dataset[1]["""file"""],
          # L
          dataset[2]["""file"""],
      ]
      __SCREAMING_SNAKE_CASE     =      object_detector(__SCREAMING_SNAKE_CASE					,							threshold=0.0			)

      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							len(__SCREAMING_SNAKE_CASE			)			)
      for outputs in batch_outputs:
           self.assertGreater(len(__SCREAMING_SNAKE_CASE			)					,							0			)
           for detected_object in outputs:
                self.assertEqual(
                    __SCREAMING_SNAKE_CASE					,							{
                        """score""": ANY(__SCREAMING_SNAKE_CASE			),
                        """label""": ANY(__SCREAMING_SNAKE_CASE			),
                        """box""": {"""xmin""": ANY(__SCREAMING_SNAKE_CASE			), """ymin""": ANY(__SCREAMING_SNAKE_CASE			), """xmax""": ANY(__SCREAMING_SNAKE_CASE			), """ymax""": ANY(__SCREAMING_SNAKE_CASE			)},
                    }					,							)
 @require_tf
 @unittest.skip("""Object detection not implemented in TF"""			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> str:


      """simple docstring"""




      pass
 @require_torch
 def       UpperCAmelCase__		(		self					:       str			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """hf-internal-testing/tiny-detr-mobilenetsv3"""

      __SCREAMING_SNAKE_CASE     =      AutoModelForObjectDetection.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							feature_extractor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,							threshold=0.0			)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]					,							threshold=0.0					,							)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              ],
              [
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      AutoModelForObjectDetection.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							feature_extractor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
              {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
              {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
              {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
              {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      0.9985
      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,							threshold=__SCREAMING_SNAKE_CASE			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)


 @require_torch
 @require_pytesseract
 @slow
 def       UpperCAmelCase__		(		self					:       int			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """Narsil/layoutlmv3-finetuned-funsd"""
      __SCREAMING_SNAKE_CASE     =      0.9993

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE					,							threshold=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
              {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
          ]					,							)






