


'''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 DetaImageProcessor






class    __magic_name__				(						unittest.TestCase							):






       def __init__(     self    ,				snake_case    ,				snake_case=7    ,				snake_case=3    ,				snake_case=3_0    ,				snake_case=4_0_0    ,				snake_case=True    ,				snake_case=None    ,				snake_case=True    ,				snake_case=[0.5, 0.5, 0.5]    ,				snake_case=[0.5, 0.5, 0.5]    ,				snake_case=True    ,				snake_case=1 / 2_5_5    ,				snake_case=True    ,				)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
          _UpperCAmelCase  :  Tuple				       =parent
          _UpperCAmelCase  :  Optional[Any]				       =batch_size
          _UpperCAmelCase  :  Any				       =num_channels
          _UpperCAmelCase  :  Union[str, Any]				       =min_resolution
          _UpperCAmelCase  :  Tuple				       =max_resolution
          _UpperCAmelCase  :  int				       =do_resize
          _UpperCAmelCase  :  List[Any]				       =size
          _UpperCAmelCase  :  Optional[int]				       =do_normalize
          _UpperCAmelCase  :  str				       =image_mean
          _UpperCAmelCase  :  str				       =image_std
          _UpperCAmelCase  :  List[str]				       =do_rescale
          _UpperCAmelCase  :  Dict				       =rescale_factor
          _UpperCAmelCase  :  int				       =do_pad






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




          '''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      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=False)		->    Union[str, Any]:




          '''simple docstring'''
          if not batched:
             _UpperCAmelCase  :  Optional[int]				       =image_inputs[0]
             if isinstance(a_    ,				Image.Image):
                _UpperCAmelCase       ,			_UpperCAmelCase  :  Tuple				       =image.size
             else:
                _UpperCAmelCase       ,			_UpperCAmelCase  :  Union[str, Any]				       =image.shape[1], image.shape[2]
             if w < h:
                _UpperCAmelCase  :  List[Any]				       =int(self.size['shortest_edge'] * h / w)
                _UpperCAmelCase  :  List[str]				       =self.size['shortest_edge']
             elif w > h:
                _UpperCAmelCase  :  List[Any]				       =self.size['shortest_edge']
                _UpperCAmelCase  :  Optional[int]				       =int(self.size['shortest_edge'] * w / h)
             else:
                _UpperCAmelCase  :  int				       =self.size['shortest_edge']
                _UpperCAmelCase  :  Any				       =self.size['shortest_edge']

          else:
             _UpperCAmelCase  :  Dict				       =[]
             for image in image_inputs:
                _UpperCAmelCase       ,			_UpperCAmelCase  :  Tuple				       =self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
             _UpperCAmelCase  :  List[str]				       =max(a_    ,				key=lambda snake_case: item[0])[0]
             _UpperCAmelCase  :  Optional[Any]				       =max(a_    ,				key=lambda snake_case: item[1])[1]

          return expected_height, expected_width






@require_torch
@require_vision
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =DetaImageProcessor if is_vision_available() else None






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =DetaImageProcessingTester(self)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()






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




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(a_    ,				'image_mean'))
          self.assertTrue(hasattr(a_    ,				'image_std'))
          self.assertTrue(hasattr(a_    ,				'do_normalize'))
          self.assertTrue(hasattr(a_    ,				'do_resize'))
          self.assertTrue(hasattr(a_    ,				'do_rescale'))
          self.assertTrue(hasattr(a_    ,				'do_pad'))
          self.assertTrue(hasattr(a_    ,				'size'))






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size    ,				{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3})
          self.assertEqual(image_processor.do_pad    ,				a_)






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




          '''simple docstring'''
          pass






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _UpperCAmelCase  :  Dict				       =prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=a_)
          for image in image_inputs:
             self.assertIsInstance(a_    ,				Image.Image)

          # Test not batched input
          _UpperCAmelCase  :  Optional[Any]				       =image_processing(image_inputs[0]    ,				return_tensors='pt').pixel_values

          _UpperCAmelCase       ,			_UpperCAmelCase  :  int				       =self.image_processor_tester.get_expected_values(a_)

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

          # Test batched
          _UpperCAmelCase       ,			_UpperCAmelCase  :  Any				       =self.image_processor_tester.get_expected_values(a_    ,				batched=a_)

          _UpperCAmelCase  :  List[str]				       =image_processing(a_    ,				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      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _UpperCAmelCase  :  Optional[Any]				       =prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=a_    ,				numpify=a_)
          for image in image_inputs:
             self.assertIsInstance(a_    ,				np.ndarray)

          # Test not batched input
          _UpperCAmelCase  :  Union[str, Any]				       =image_processing(image_inputs[0]    ,				return_tensors='pt').pixel_values

          _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =self.image_processor_tester.get_expected_values(a_)

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

          # Test batched
          _UpperCAmelCase  :  str				       =image_processing(a_    ,				return_tensors='pt').pixel_values

          _UpperCAmelCase       ,			_UpperCAmelCase  :  str				       =self.image_processor_tester.get_expected_values(a_    ,				batched=a_)

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






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




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _UpperCAmelCase  :  int				       =prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=a_    ,				torchify=a_)
          for image in image_inputs:
             self.assertIsInstance(a_    ,				torch.Tensor)

          # Test not batched input
          _UpperCAmelCase  :  Tuple				       =image_processing(image_inputs[0]    ,				return_tensors='pt').pixel_values

          _UpperCAmelCase       ,			_UpperCAmelCase  :  Dict				       =self.image_processor_tester.get_expected_values(a_)

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

          # Test batched
          _UpperCAmelCase  :  Dict				       =image_processing(a_    ,				return_tensors='pt').pixel_values

          _UpperCAmelCase       ,			_UpperCAmelCase  :  List[Any]				       =self.image_processor_tester.get_expected_values(a_    ,				batched=a_)

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






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




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
          with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,				'r') as f:
             _UpperCAmelCase  :  Dict				       =json.loads(f.read())

          _UpperCAmelCase  :  Tuple				       ={'image_id': 3_9_7_6_9, 'annotations': target}

          # encode them
          _UpperCAmelCase  :  Optional[Any]				       =DetaImageProcessor()
          _UpperCAmelCase  :  List[str]				       =image_processing(images=a_    ,				annotations=a_    ,				return_tensors='pt')

          # verify pixel values
          _UpperCAmelCase  :  Tuple				       =torch.Size([1, 3, 8_0_0, 1_0_6_6])
          self.assertEqual(encoding['pixel_values'].shape    ,				a_)

          _UpperCAmelCase  :  List[str]				       =torch.tensor([0.27_96, 0.31_38, 0.34_81])
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,				a_    ,				atol=1E-4))

          # verify area
          _UpperCAmelCase  :  Union[str, Any]				       =torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,				a_))
          # verify boxes
          _UpperCAmelCase  :  str				       =torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape    ,				a_)
          _UpperCAmelCase  :  Dict				       =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,				a_    ,				atol=1E-3))
          # verify image_id
          _UpperCAmelCase  :  Tuple				       =torch.tensor([3_9_7_6_9])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,				a_))
          # verify is_crowd
          _UpperCAmelCase  :  Union[str, Any]				       =torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,				a_))
          # verify class_labels
          _UpperCAmelCase  :  List[str]				       =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,				a_))
          # verify orig_size
          _UpperCAmelCase  :  Any				       =torch.tensor([4_8_0, 6_4_0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,				a_))
          # verify size
          _UpperCAmelCase  :  Optional[int]				       =torch.tensor([8_0_0, 1_0_6_6])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,				a_))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
          with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,				'r') as f:
             _UpperCAmelCase  :  Optional[int]				       =json.loads(f.read())

          _UpperCAmelCase  :  str				       ={'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

          _UpperCAmelCase  :  str				       =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

          # encode them
          _UpperCAmelCase  :  Union[str, Any]				       =DetaImageProcessor(format='coco_panoptic')
          _UpperCAmelCase  :  Optional[int]				       =image_processing(images=a_    ,				annotations=a_    ,				masks_path=a_    ,				return_tensors='pt')

          # verify pixel values
          _UpperCAmelCase  :  str				       =torch.Size([1, 3, 8_0_0, 1_0_6_6])
          self.assertEqual(encoding['pixel_values'].shape    ,				a_)

          _UpperCAmelCase  :  Tuple				       =torch.tensor([0.27_96, 0.31_38, 0.34_81])
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,				a_    ,				atol=1E-4))

          # verify area
          _UpperCAmelCase  :  Tuple				       =torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,				a_))
          # verify boxes
          _UpperCAmelCase  :  Optional[int]				       =torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape    ,				a_)
          _UpperCAmelCase  :  Dict				       =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,				a_    ,				atol=1E-3))
          # verify image_id
          _UpperCAmelCase  :  str				       =torch.tensor([3_9_7_6_9])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,				a_))
          # verify is_crowd
          _UpperCAmelCase  :  Optional[int]				       =torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,				a_))
          # verify class_labels
          _UpperCAmelCase  :  int				       =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,				a_))
          # verify masks
          _UpperCAmelCase  :  Tuple				       =8_2_2_8_7_3
          self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,				a_)
          # verify orig_size
          _UpperCAmelCase  :  Optional[Any]				       =torch.tensor([4_8_0, 6_4_0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,				a_))
          # verify size
          _UpperCAmelCase  :  str				       =torch.tensor([8_0_0, 1_0_6_6])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,				a_))






def     UpperCAmelCase_		(					)	->     int:



				"""simple docstring"""





				return 1







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else two_pence(x - 2					) + one_pence()







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else five_pence(x - 5					) + two_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else ten_pence(x - 10					) + five_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else twenty_pence(x - 20					) + ten_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else fifty_pence(x - 50					) + twenty_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else one_pound(x - 100					) + fifty_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else two_pound(x - 200					) + one_pound(snake_case__					)







def     UpperCAmelCase_		(					snake_case__ = 200					)	->     int:



				"""simple docstring"""





				return two_pound(snake_case__					)


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