






"""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 YolosImageProcessor






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def __init__(			self      :						int			,    snake_case      :						str			,    snake_case      :						List[str]=7			,    snake_case      :						List[Any]=3			,    snake_case      :						int=30			,    snake_case      :						Dict=400			,    snake_case      :						str=True			,    snake_case      :						Optional[int]=None			,    snake_case      :						List[str]=True			,    snake_case      :						List[Any]=[0.5, 0.5, 0.5]			,    snake_case      :						Tuple=[0.5, 0.5, 0.5]			,    snake_case      :						Union[str, Any]=True			,    snake_case      :						List[str]=1 / 255			,    snake_case      :						Tuple=True			,    ):
													'''simple docstring'''





													A__						:  List[str]		  =  size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
													A__						:  str		  =  parent
													A__						:  Dict		  =  batch_size
													A__						:  Dict		  =  num_channels
													A__						:  Union[str, Any]		  =  min_resolution
													A__						:  List[Any]		  =  max_resolution
													A__						:  Any		  =  do_resize
													A__						:  List[Any]		  =  size
													A__						:  Optional[Any]		  =  do_normalize
													A__						:  List[Any]		  =  image_mean
													A__						:  Optional[int]		  =  image_std
													A__						:  Optional[Any]		  =  do_rescale
													A__						:  int		  =  rescale_factor
													A__						:  str		  =  do_pad


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''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 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Dict			,    snake_case      :						str=False							):
													'''simple docstring'''





													if not batched:
																			A__						:  Any		  =  image_inputs[0]
																			if isinstance(snake_case			,    Image.Image							):
																									A__  ,  A__						:  Optional[Any]		  =  image.size
																			else:
																									A__  ,  A__						:  str		  =  image.shape[1], image.shape[2]
																			if w < h:
																									A__						:  Any		  =  int(self.size["""shortest_edge"""] * h / w							)
																									A__						:  Optional[int]		  =  self.size["""shortest_edge"""]
																			elif w > h:
																									A__						:  Optional[int]		  =  self.size["""shortest_edge"""]
																									A__						:  List[Any]		  =  int(self.size["""shortest_edge"""] * w / h							)
																			else:
																									A__						:  Tuple		  =  self.size["""shortest_edge"""]
																									A__						:  Dict		  =  self.size["""shortest_edge"""]

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

													return expected_height, expected_width






@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							YolosImageProcessor if is_vision_available() else None


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[str]		  =  YolosImageProcessingTester(self							)


							@property
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													return self.image_processor_tester.prepare_image_processor_dict()


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  str		  =  self.image_processing_class(**self.image_processor_dict							)
													self.assertTrue(hasattr(snake_case			,    """image_mean"""							)							)
													self.assertTrue(hasattr(snake_case			,    """image_std"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_normalize"""							)							)
													self.assertTrue(hasattr(snake_case			,    """do_resize"""							)							)
													self.assertTrue(hasattr(snake_case			,    """size"""							)							)


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  int		  =  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			,    snake_case							)

													A__						:  Optional[Any]		  =  self.image_processing_class.from_dict(
													    self.image_processor_dict			,    size=42			,    max_size=84			,    pad_and_return_pixel_mask=snake_case							)
													self.assertEqual(image_processor.size			,    {"""shortest_edge""": 42, """longest_edge""": 84}							)
													self.assertEqual(image_processor.do_pad			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Dict		  =  self.image_processing_class(**self.image_processor_dict							)
													# create random PIL images
													A__						:  List[str]		  =  prepare_image_inputs(self.image_processor_tester			,    equal_resolution=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    Image.Image							)

													# Test not batched input
													A__						:  List[Any]		  =  image_processing(image_inputs[0]			,    return_tensors="""pt"""							).pixel_values

													A__  ,  A__						:  Optional[int]		  =  self.image_processor_tester.get_expected_values(snake_case							)

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

													# Test batched
													A__  ,  A__						:  Union[str, Any]		  =  self.image_processor_tester.get_expected_values(snake_case			,    batched=snake_case							)

													A__						:  List[str]		  =  image_processing(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 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  self.image_processing_class(**self.image_processor_dict							)
													# create random numpy tensors
													A__						:  List[str]		  =  prepare_image_inputs(self.image_processor_tester			,    equal_resolution=snake_case			,    numpify=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    np.ndarray							)

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

													A__  ,  A__						:  Dict		  =  self.image_processor_tester.get_expected_values(snake_case							)

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

													# Test batched
													A__						:  Optional[int]		  =  image_processing(snake_case			,    return_tensors="""pt"""							).pixel_values

													A__  ,  A__						:  Tuple		  =  self.image_processor_tester.get_expected_values(snake_case			,    batched=snake_case							)

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


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





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

													# Test not batched input
													A__						:  Optional[int]		  =  image_processing(image_inputs[0]			,    return_tensors="""pt"""							).pixel_values

													A__  ,  A__						:  List[str]		  =  self.image_processor_tester.get_expected_values(snake_case							)

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

													# Test batched
													A__						:  Union[str, Any]		  =  image_processing(snake_case			,    return_tensors="""pt"""							).pixel_values

													A__  ,  A__						:  List[Any]		  =  self.image_processor_tester.get_expected_values(snake_case			,    batched=snake_case							)

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


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.image_processing_class(**self.image_processor_dict							)
													A__						:  List[Any]		  =  self.image_processing_class(do_resize=snake_case			,    do_normalize=snake_case			,    do_rescale=snake_case							)
													# create random PyTorch tensors
													A__						:  Union[str, Any]		  =  prepare_image_inputs(self.image_processor_tester			,    equal_resolution=snake_case			,    torchify=snake_case							)
													for image in image_inputs:
																			self.assertIsInstance(snake_case			,    torch.Tensor							)

													# Test whether the method "pad" and calling the image processor return the same tensors
													A__						:  Any		  =  image_processing_a.pad(snake_case			,    return_tensors="""pt"""							)
													A__						:  Optional[Any]		  =  image_processing_a(snake_case			,    return_tensors="""pt"""							)

													self.assertTrue(
													    torch.allclose(encoded_images_with_method["""pixel_values"""]			,    encoded_images["""pixel_values"""]			,    atol=1e-4							)							)


							@slow
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





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

													A__						:  str		  =  {"""image_id""": 3_9769, """annotations""": target}

													# encode them
													A__						:  Tuple		  =  YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""							)
													A__						:  Optional[Any]		  =  image_processing(images=snake_case			,    annotations=snake_case			,    return_tensors="""pt"""							)

													# verify pixel values
													A__						:  str		  =  torch.Size([1, 3, 800, 1066]							)
													self.assertEqual(encoding["""pixel_values"""].shape			,    snake_case							)

													A__						:  Any		  =  torch.tensor([0.2796, 0.3138, 0.3481]							)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,    snake_case			,    atol=1e-4							)							)

													# verify area
													A__						:  Optional[int]		  =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,    snake_case							)							)
													# verify boxes
													A__						:  List[str]		  =  torch.Size([6, 4]							)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,    snake_case							)
													A__						:  str		  =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,    snake_case			,    atol=1e-3							)							)
													# verify image_id
													A__						:  int		  =  torch.tensor([3_9769]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,    snake_case							)							)
													# verify is_crowd
													A__						:  Tuple		  =  torch.tensor([0, 0, 0, 0, 0, 0]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,    snake_case							)							)
													# verify class_labels
													A__						:  Union[str, Any]		  =  torch.tensor([75, 75, 63, 65, 17, 17]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,    snake_case							)							)
													# verify orig_size
													A__						:  str		  =  torch.tensor([480, 640]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,    snake_case							)							)
													# verify size
													A__						:  Optional[int]		  =  torch.tensor([800, 1066]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,    snake_case							)							)


							@slow
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





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

													A__						:  Optional[int]		  =  {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

													A__						:  List[Any]		  =  pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

													# encode them
													A__						:  Union[str, Any]		  =  YolosImageProcessor(format="""coco_panoptic"""							)
													A__						:  Optional[int]		  =  image_processing(images=snake_case			,    annotations=snake_case			,    masks_path=snake_case			,    return_tensors="""pt"""							)

													# verify pixel values
													A__						:  str		  =  torch.Size([1, 3, 800, 1066]							)
													self.assertEqual(encoding["""pixel_values"""].shape			,    snake_case							)

													A__						:  List[str]		  =  torch.tensor([0.2796, 0.3138, 0.3481]							)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,    snake_case			,    atol=1e-4							)							)

													# verify area
													A__						:  Optional[int]		  =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,    snake_case							)							)
													# verify boxes
													A__						:  int		  =  torch.Size([6, 4]							)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,    snake_case							)
													A__						:  List[Any]		  =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,    snake_case			,    atol=1e-3							)							)
													# verify image_id
													A__						:  str		  =  torch.tensor([3_9769]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,    snake_case							)							)
													# verify is_crowd
													A__						:  Optional[Any]		  =  torch.tensor([0, 0, 0, 0, 0, 0]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,    snake_case							)							)
													# verify class_labels
													A__						:  Optional[int]		  =  torch.tensor([17, 17, 63, 75, 75, 93]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,    snake_case							)							)
													# verify masks
													A__						:  Dict		  =  82_2873
													self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()			,    snake_case							)
													# verify orig_size
													A__						:  Tuple		  =  torch.tensor([480, 640]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,    snake_case							)							)
													# verify size
													A__						:  Tuple		  =  torch.tensor([800, 1066]							)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,    snake_case							)							)














"""simple docstring"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					int = 1_0_0_0      )		->int:
						A__						:  Optional[int]		  =  2**power
						A__						:  Dict		  =  0
						while n:
												A__  ,  A__						:  Tuple		  =  r + n % 1_0, n // 1_0
						return r


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






