


'''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      a    (		unittest.TestCase	):


     """simple docstring"""






     def __init__(    self					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		Tuple=7							,			snake_case					:		List[Any]=3							,			snake_case					:		Any=30							,			snake_case					:		List[Any]=400							,			snake_case					:		Any=True							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		Tuple=True							,			snake_case					:		int=[0.5, 0.5, 0.5]							,			snake_case					:		Tuple=[0.5, 0.5, 0.5]							,			snake_case					:		List[str]=True							,			snake_case					:		Any=1 / 255							,			snake_case					:		Dict=True							,			)						->							Tuple:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          __UpperCAmelCase     :			Optional[Any]  							=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          __UpperCAmelCase     :			str  							=      parent
          __UpperCAmelCase     :			Dict  							=      batch_size
          __UpperCAmelCase     :			str  							=      num_channels
          __UpperCAmelCase     :			List[str]  							=      min_resolution
          __UpperCAmelCase     :			Dict  							=      max_resolution
          __UpperCAmelCase     :			Optional[Any]  							=      do_resize
          __UpperCAmelCase     :			Dict  							=      size
          __UpperCAmelCase     :			List[str]  							=      do_normalize
          __UpperCAmelCase     :			Optional[Any]  							=      image_mean
          __UpperCAmelCase     :			Union[str, Any]  							=      image_std
          __UpperCAmelCase     :			Union[str, Any]  							=      do_rescale
          __UpperCAmelCase     :			Tuple  							=      rescale_factor
          __UpperCAmelCase     :			List[str]  							=      do_pad

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Any:
          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					:		List[str]							,			snake_case					:		List[Any]							,			snake_case					:		Optional[Any]=False      )						->							str:
          if not batched:
               __UpperCAmelCase     :			Union[str, Any]  							=      image_inputs[0]
               if isinstance(snake_case							,			Image.Image      ):
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Optional[Any]  							=      image.size
               else:
                    __UpperCAmelCase							,       __UpperCAmelCase     :			List[Any]  							=      image.shape[1], image.shape[2]
               if w < h:
                    __UpperCAmelCase     :			List[str]  							=      int(self.size['''shortest_edge'''] * h / w      )
                    __UpperCAmelCase     :			Optional[int]  							=      self.size['''shortest_edge''']
               elif w > h:
                    __UpperCAmelCase     :			Tuple  							=      self.size['''shortest_edge''']
                    __UpperCAmelCase     :			int  							=      int(self.size['''shortest_edge'''] * w / h      )
               else:
                    __UpperCAmelCase     :			Tuple  							=      self.size['''shortest_edge''']
                    __UpperCAmelCase     :			int  							=      self.size['''shortest_edge''']

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

          return expected_height, expected_width



@require_torch
@require_vision
class      a    (		_a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       YolosImageProcessor if is_vision_available() else None

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Tuple:
          __UpperCAmelCase     :			Union[str, Any]  							=      YolosImageProcessingTester(self      )

     @property
     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Tuple:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 			lowerCamelCase__			(    self					:		str      )						->							Tuple:
          __UpperCAmelCase     :			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 			lowerCamelCase__			(    self					:		int      )						->							str:
          __UpperCAmelCase     :			Union[str, Any]  							=      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      )

          __UpperCAmelCase     :			Union[str, 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 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          pass

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          # Initialize image_processing
          __UpperCAmelCase     :			Tuple  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          __UpperCAmelCase     :			int  							=      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
          __UpperCAmelCase     :			Optional[Any]  							=      image_processing(image_inputs[0]							,			return_tensors='''pt'''      ).pixel_values

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

          __UpperCAmelCase     :			Optional[int]  							=      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 			lowerCamelCase__			(    self					:		str      )						->							List[Any]:
          # Initialize image_processing
          __UpperCAmelCase     :			List[str]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          __UpperCAmelCase     :			Optional[int]  							=      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
          __UpperCAmelCase     :			int  							=      image_processing(image_inputs[0]							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			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
          __UpperCAmelCase     :			List[str]  							=      image_processing(snake_case							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      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 			lowerCamelCase__			(    self					:		List[Any]      )						->							Tuple:
          # Initialize image_processing
          __UpperCAmelCase     :			Optional[int]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          __UpperCAmelCase     :			Tuple  							=      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
          __UpperCAmelCase     :			List[Any]  							=      image_processing(image_inputs[0]							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			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
          __UpperCAmelCase     :			str  							=      image_processing(snake_case							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			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 			lowerCamelCase__			(    self					:		List[Any]      )						->							Any:
          # Initialize image_processings
          __UpperCAmelCase     :			Optional[int]  							=      self.image_processing_class(**self.image_processor_dict      )
          __UpperCAmelCase     :			Tuple  							=      self.image_processing_class(do_resize=snake_case							,			do_normalize=snake_case							,			do_rescale=snake_case      )
          # create random PyTorch tensors
          __UpperCAmelCase     :			int  							=      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
          __UpperCAmelCase     :			Any  							=      image_processing_a.pad(snake_case							,			return_tensors='''pt'''      )
          __UpperCAmelCase     :			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 			lowerCamelCase__			(    self					:		Any      )						->							Tuple:
          # prepare image and target
          __UpperCAmelCase     :			Optional[int]  							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,			'''r'''      ) as f:
               __UpperCAmelCase     :			List[str]  							=      json.loads(f.read()      )

          __UpperCAmelCase     :			List[str]  							=      {'''image_id''': 3_9769, '''annotations''': target}

          # encode them
          __UpperCAmelCase     :			Optional[int]  							=      YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''      )
          __UpperCAmelCase     :			str  							=      image_processing(images=snake_case							,			annotations=snake_case							,			return_tensors='''pt'''      )

          # verify pixel values
          __UpperCAmelCase     :			Optional[int]  							=      torch.Size([1, 3, 800, 1066]      )
          self.assertEqual(encoding['''pixel_values'''].shape							,			snake_case      )

          __UpperCAmelCase     :			str  							=      torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,			snake_case							,			atol=1E-4      )      )

          # verify area
          __UpperCAmelCase     :			str  							=      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,			snake_case      )      )
          # verify boxes
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,			snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,			snake_case							,			atol=1E-3      )      )
          # verify image_id
          __UpperCAmelCase     :			int  							=      torch.tensor([3_9769]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,			snake_case      )      )
          # verify is_crowd
          __UpperCAmelCase     :			int  							=      torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,			snake_case      )      )
          # verify class_labels
          __UpperCAmelCase     :			List[Any]  							=      torch.tensor([75, 75, 63, 65, 17, 17]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,			snake_case      )      )
          # verify orig_size
          __UpperCAmelCase     :			str  							=      torch.tensor([480, 640]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,			snake_case      )      )
          # verify size
          __UpperCAmelCase     :			Tuple  							=      torch.tensor([800, 1066]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,			snake_case      )      )






     @slow
     def 			lowerCamelCase__			(    self					:		str      )						->							List[Any]:
          # prepare image, target and masks_path
          __UpperCAmelCase     :			int  							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,			'''r'''      ) as f:
               __UpperCAmelCase     :			int  							=      json.loads(f.read()      )

          __UpperCAmelCase     :			Tuple  							=      {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

          __UpperCAmelCase     :			Optional[Any]  							=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

          # encode them
          __UpperCAmelCase     :			Any  							=      YolosImageProcessor(format='''coco_panoptic'''      )
          __UpperCAmelCase     :			Tuple  							=      image_processing(images=snake_case							,			annotations=snake_case							,			masks_path=snake_case							,			return_tensors='''pt'''      )

          # verify pixel values
          __UpperCAmelCase     :			Optional[Any]  							=      torch.Size([1, 3, 800, 1066]      )
          self.assertEqual(encoding['''pixel_values'''].shape							,			snake_case      )

          __UpperCAmelCase     :			Dict  							=      torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,			snake_case							,			atol=1E-4      )      )

          # verify area
          __UpperCAmelCase     :			int  							=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,			snake_case      )      )
          # verify boxes
          __UpperCAmelCase     :			List[Any]  							=      torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,			snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,			snake_case							,			atol=1E-3      )      )
          # verify image_id
          __UpperCAmelCase     :			Any  							=      torch.tensor([3_9769]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,			snake_case      )      )
          # verify is_crowd
          __UpperCAmelCase     :			Tuple  							=      torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,			snake_case      )      )
          # verify class_labels
          __UpperCAmelCase     :			Any  							=      torch.tensor([17, 17, 63, 75, 75, 93]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,			snake_case      )      )
          # verify masks
          __UpperCAmelCase     :			Union[str, Any]  							=      82_2873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,			snake_case      )
          # verify orig_size
          __UpperCAmelCase     :			Dict  							=      torch.tensor([480, 640]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,			snake_case      )      )
          # verify size
          __UpperCAmelCase     :			Optional[int]  							=      torch.tensor([800, 1066]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,			snake_case      )      )



'''simple docstring'''



def      _a     (						_lowercase :     List[str]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      1
     __UpperCAmelCase     :			List[str]  							=      2
     while i * i <= n:
          __UpperCAmelCase     :			Optional[Any]  							=      0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      1
     __UpperCAmelCase     :			List[Any]  							=      1

     while True:
          i += 1
          t_num += i

          if count_divisors(_lowercase  ) > 500:
               break

     return t_num


if __name__ == "__main__":
      print(solution())