





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							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   List[str]=7							,     __lowerCAmelCase       :   Any=3							,     __lowerCAmelCase       :   str=30							,     __lowerCAmelCase       :   Tuple=4_00							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Optional[Any]=None							,     __lowerCAmelCase       :   Tuple=True							,     __lowerCAmelCase       :   Optional[Any]=[0.5, 0.5, 0.5]							,     __lowerCAmelCase       :   Dict=[0.5, 0.5, 0.5]							,     __lowerCAmelCase       :   Tuple=True							,     __lowerCAmelCase       :   Dict=1 / 2_55							,     __lowerCAmelCase       :   str=True							,     )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
					A__  				=       parent
					A__  				=       batch_size
					A__  				=       num_channels
					A__  				=       min_resolution
					A__  				=       max_resolution
					A__  				=       do_resize
					A__  				=       size
					A__  				=       do_normalize
					A__  				=       image_mean
					A__  				=       image_std
					A__  				=       do_rescale
					A__  				=       rescale_factor
					A__  				=       do_pad






			def        a_	(      self       :   Tuple       )     ->     int:







					"""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       :   Any							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   int=False       )     ->     List[Any]:







					"""simple docstring"""






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

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

					return expected_height, expected_width


@require_torch
@require_vision
class   A      (SCREAMING_SNAKE_CASE       ,					unittest.TestCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   YolosImageProcessor if is_vision_available() else None






			def        a_	(      self       :   Optional[int]       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       YolosImageProcessingTester(self       )






			@property
			def        a_	(      self       :   Dict       )     ->     Tuple:







					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()






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







					"""simple docstring"""






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






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







					"""simple docstring"""






					A__  				=       self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size							,     {"""shortest_edge""": 18, """longest_edge""": 13_33}       )
					self.assertEqual(image_processor.do_pad							,     __lowerCAmelCase       )

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






			def        a_	(      self       :   Tuple       )     ->     int:







					"""simple docstring"""






					pass






			def        a_	(      self       :   Dict       )     ->     int:







					"""simple docstring"""






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

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

					A__				,      A__  				=       self.image_processor_tester.get_expected_values(__lowerCAmelCase       )

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

					# Test batched
					A__				,      A__  				=       self.image_processor_tester.get_expected_values(__lowerCAmelCase							,     batched=__lowerCAmelCase       )

					A__  				=       image_processing(__lowerCAmelCase							,     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       :   Dict       )     ->     Tuple:







					"""simple docstring"""






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

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

					A__				,      A__  				=       self.image_processor_tester.get_expected_values(__lowerCAmelCase       )

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

					# Test batched
					A__  				=       image_processing(__lowerCAmelCase							,     return_tensors="""pt"""       ).pixel_values

					A__				,      A__  				=       self.image_processor_tester.get_expected_values(__lowerCAmelCase							,     batched=__lowerCAmelCase       )

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






			def        a_	(      self       :   Any       )     ->     Tuple:







					"""simple docstring"""






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

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

					A__				,      A__  				=       self.image_processor_tester.get_expected_values(__lowerCAmelCase       )

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

					# Test batched
					A__  				=       image_processing(__lowerCAmelCase							,     return_tensors="""pt"""       ).pixel_values

					A__				,      A__  				=       self.image_processor_tester.get_expected_values(__lowerCAmelCase							,     batched=__lowerCAmelCase       )

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






			def        a_	(      self       :   Optional[Any]       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       self.image_processing_class(**self.image_processor_dict       )
					A__  				=       self.image_processing_class(do_resize=__lowerCAmelCase							,     do_normalize=__lowerCAmelCase							,     do_rescale=__lowerCAmelCase       )
					# create random PyTorch tensors
					A__  				=       prepare_image_inputs(self.image_processor_tester							,     equal_resolution=__lowerCAmelCase							,     torchify=__lowerCAmelCase       )
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase							,     torch.Tensor       )

					# Test whether the method "pad" and calling the image processor return the same tensors
					A__  				=       image_processing_a.pad(__lowerCAmelCase							,     return_tensors="""pt"""       )
					A__  				=       image_processing_a(__lowerCAmelCase							,     return_tensors="""pt"""       )

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






			@slow
			def        a_	(      self       :   Optional[int]       )     ->     List[str]:







					"""simple docstring"""






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

					A__  				=       {"""image_id""": 3_97_69, """annotations""": target}

					# encode them
					A__  				=       YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""       )
					A__  				=       image_processing(images=__lowerCAmelCase							,     annotations=__lowerCAmelCase							,     return_tensors="""pt"""       )

					# verify pixel values
					A__  				=       torch.Size([1, 3, 8_00, 10_66]       )
					self.assertEqual(encoding["""pixel_values"""].shape							,     __lowerCAmelCase       )

					A__  				=       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]							,     __lowerCAmelCase							,     atol=1e-4       )       )

					# verify area
					A__  				=       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]							,     __lowerCAmelCase       )       )
					# verify boxes
					A__  				=       torch.Size([6, 4]       )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,     __lowerCAmelCase       )
					A__  				=       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]							,     __lowerCAmelCase							,     atol=1e-3       )       )
					# verify image_id
					A__  				=       torch.tensor([3_97_69]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,     __lowerCAmelCase       )       )
					# verify is_crowd
					A__  				=       torch.tensor([0, 0, 0, 0, 0, 0]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,     __lowerCAmelCase       )       )
					# verify class_labels
					A__  				=       torch.tensor([75, 75, 63, 65, 17, 17]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,     __lowerCAmelCase       )       )
					# verify orig_size
					A__  				=       torch.tensor([4_80, 6_40]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,     __lowerCAmelCase       )       )
					# verify size
					A__  				=       torch.tensor([8_00, 10_66]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,     __lowerCAmelCase       )       )







			@slow
			def        a_	(      self       :   str       )     ->     Optional[int]:







					"""simple docstring"""






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

					A__  				=       {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

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

					# encode them
					A__  				=       YolosImageProcessor(format="""coco_panoptic"""       )
					A__  				=       image_processing(images=__lowerCAmelCase							,     annotations=__lowerCAmelCase							,     masks_path=__lowerCAmelCase							,     return_tensors="""pt"""       )

					# verify pixel values
					A__  				=       torch.Size([1, 3, 8_00, 10_66]       )
					self.assertEqual(encoding["""pixel_values"""].shape							,     __lowerCAmelCase       )

					A__  				=       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]							,     __lowerCAmelCase							,     atol=1e-4       )       )

					# verify area
					A__  				=       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]							,     __lowerCAmelCase       )       )
					# verify boxes
					A__  				=       torch.Size([6, 4]       )
					self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,     __lowerCAmelCase       )
					A__  				=       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]							,     __lowerCAmelCase							,     atol=1e-3       )       )
					# verify image_id
					A__  				=       torch.tensor([3_97_69]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,     __lowerCAmelCase       )       )
					# verify is_crowd
					A__  				=       torch.tensor([0, 0, 0, 0, 0, 0]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,     __lowerCAmelCase       )       )
					# verify class_labels
					A__  				=       torch.tensor([17, 17, 63, 75, 75, 93]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,     __lowerCAmelCase       )       )
					# verify masks
					A__  				=       82_28_73
					self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()							,     __lowerCAmelCase       )
					# verify orig_size
					A__  				=       torch.tensor([4_80, 6_40]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,     __lowerCAmelCase       )       )
					# verify size
					A__  				=       torch.tensor([8_00, 10_66]       )
					self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,     __lowerCAmelCase       )       )










import math







def        __lowerCamelCase (							__a       :int   )		->    bool:






		"""simple docstring"""

		A__  				=       math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

		return exponent == int(__a   )







def        __lowerCamelCase (							__a       :float = 1 / 1_2_3_4_5   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       0

		A__  				=       3
		while True:
				A__  				=       (integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(__a   ):
						A__  				=       int(__a   )
						total_partitions += 1
						if check_partition_perfect(__a   ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(__a   )
				integer += 1


if __name__ == "__main__":
					print(F'''{solution() = }''')



