



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 ConditionalDetrImageProcessor






class 						__a (     unittest.TestCase				):



	def __init__(						self							:    int				,	snake_case_							:    int				,	snake_case_							:    List[Any]=7				,	snake_case_							:    str=3				,	snake_case_							:    Union[str, Any]=30				,	snake_case_							:    Optional[int]=4_00				,	snake_case_							:    List[str]=True				,	snake_case_							:    List[Any]=None				,	snake_case_							:    Union[str, Any]=True				,	snake_case_							:    Union[str, Any]=[0.5, 0.5, 0.5]				,	snake_case_							:    Optional[int]=[0.5, 0.5, 0.5]				,	snake_case_							:    Dict=True				,	snake_case_							:    Optional[Any]=1 / 2_55				,	snake_case_							:    Any=True				,	)->							List[Any]:
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						__lowerCAmelCase	   =size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
						__lowerCAmelCase	   =parent
						__lowerCAmelCase	   =batch_size
						__lowerCAmelCase	   =num_channels
						__lowerCAmelCase	   =min_resolution
						__lowerCAmelCase	   =max_resolution
						__lowerCAmelCase	   =do_resize
						__lowerCAmelCase	   =size
						__lowerCAmelCase	   =do_normalize
						__lowerCAmelCase	   =image_mean
						__lowerCAmelCase	   =image_std
						__lowerCAmelCase	   =do_rescale
						__lowerCAmelCase	   =rescale_factor
						__lowerCAmelCase	   =do_pad



	def 		UpperCamelCase							(						self							:    List[str])->							Union[str, 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 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict=False)->							Optional[int]:
						if not batched:
											__lowerCAmelCase	   =image_inputs[0]
											if isinstance(snake_case_				,	Image.Image):
																__lowerCAmelCase   ,	__lowerCAmelCase	   =image.size
											else:
																__lowerCAmelCase   ,	__lowerCAmelCase	   =image.shape[1], image.shape[2]
											if w < h:
																__lowerCAmelCase	   =int(self.size["""shortest_edge"""] * h / w)
																__lowerCAmelCase	   =self.size["""shortest_edge"""]
											elif w > h:
																__lowerCAmelCase	   =self.size["""shortest_edge"""]
																__lowerCAmelCase	   =int(self.size["""shortest_edge"""] * w / h)
											else:
																__lowerCAmelCase	   =self.size["""shortest_edge"""]
																__lowerCAmelCase	   =self.size["""shortest_edge"""]

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

						return expected_height, expected_width






@require_torch
@require_vision
class 						__a (     SCREAMING_SNAKE_CASE	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       ConditionalDetrImageProcessor if is_vision_available() else None



	def 		UpperCamelCase							(						self							:    Optional[Any])->							int:
						__lowerCAmelCase	   =ConditionalDetrImageProcessingTester(self)



	@property
	def 		UpperCamelCase							(						self							:    str)->							Any:
						return self.image_processor_tester.prepare_image_processor_dict()



	def 		UpperCamelCase							(						self							:    Any)->							Tuple:
						__lowerCAmelCase	   =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							:    List[Any])->							List[Any]:
						__lowerCAmelCase	   =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				,	snake_case_)

						__lowerCAmelCase	   =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							:    int)->							Optional[int]:
						pass



	def 		UpperCamelCase							(						self							:    int)->							str:
						# Initialize image_processing
						__lowerCAmelCase	   =self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase	   =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
						__lowerCAmelCase	   =image_processing(image_inputs[0]				,	return_tensors="""pt""").pixel_values

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

						__lowerCAmelCase	   =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							:    int)->							Tuple:
						# Initialize image_processing
						__lowerCAmelCase	   =self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase	   =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
						__lowerCAmelCase	   =image_processing(image_inputs[0]				,	return_tensors="""pt""").pixel_values

						__lowerCAmelCase   ,	__lowerCAmelCase	   =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
						__lowerCAmelCase	   =image_processing(snake_case_				,	return_tensors="""pt""").pixel_values

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

						__lowerCAmelCase   ,	__lowerCAmelCase	   =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
						__lowerCAmelCase	   =image_processing(snake_case_				,	return_tensors="""pt""").pixel_values

						__lowerCAmelCase   ,	__lowerCAmelCase	   =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,
						    )				,	)



	@slow
	def 		UpperCamelCase							(						self							:    Optional[Any])->							int:
						# prepare image and target
						__lowerCAmelCase	   =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,	"""r""") as f:
											__lowerCAmelCase	   =json.loads(f.read())

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

						# encode them
						__lowerCAmelCase	   =ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""")
						__lowerCAmelCase	   =image_processing(images=snake_case_				,	annotations=snake_case_				,	return_tensors="""pt""")

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

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

						# verify area
						__lowerCAmelCase	   =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"""]				,	snake_case_))
						# verify boxes
						__lowerCAmelCase	   =torch.Size([6, 4])
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,	snake_case_)
						__lowerCAmelCase	   =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]				,	snake_case_				,	atol=1e-3))
						# verify image_id
						__lowerCAmelCase	   =torch.tensor([3_97_69])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,	snake_case_))
						# verify is_crowd
						__lowerCAmelCase	   =torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,	snake_case_))
						# verify class_labels
						__lowerCAmelCase	   =torch.tensor([75, 75, 63, 65, 17, 17])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,	snake_case_))
						# verify orig_size
						__lowerCAmelCase	   =torch.tensor([4_80, 6_40])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,	snake_case_))
						# verify size
						__lowerCAmelCase	   =torch.tensor([8_00, 10_66])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,	snake_case_))



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

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

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

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

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

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

						# verify area
						__lowerCAmelCase	   =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"""]				,	snake_case_))
						# verify boxes
						__lowerCAmelCase	   =torch.Size([6, 4])
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,	snake_case_)
						__lowerCAmelCase	   =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]				,	snake_case_				,	atol=1e-3))
						# verify image_id
						__lowerCAmelCase	   =torch.tensor([3_97_69])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,	snake_case_))
						# verify is_crowd
						__lowerCAmelCase	   =torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,	snake_case_))
						# verify class_labels
						__lowerCAmelCase	   =torch.tensor([17, 17, 63, 75, 75, 93])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,	snake_case_))
						# verify masks
						__lowerCAmelCase	   =82_28_73
						self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,	snake_case_)
						# verify orig_size
						__lowerCAmelCase	   =torch.tensor([4_80, 6_40])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,	snake_case_))
						# verify size
						__lowerCAmelCase	   =torch.tensor([8_00, 10_66])
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,	snake_case_))









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					__lowerCAmelCase	   =False
					while is_sorted is False:  # Until all the indices are traversed keep looping
										__lowerCAmelCase	   =True
										for i in range(0				,   len(__lowerCamelCase	) - 1				,   2	):  # iterating over all even indices
															if input_list[i] > input_list[i + 1]:
																				__lowerCAmelCase   ,	__lowerCAmelCase	   =input_list[i + 1], input_list[i]
																				# swapping if elements not in order
																				__lowerCAmelCase	   =False

										for i in range(1				,   len(__lowerCamelCase	) - 1				,   2	):  # iterating over all odd indices
															if input_list[i] > input_list[i + 1]:
																				__lowerCAmelCase   ,	__lowerCAmelCase	   =input_list[i + 1], input_list[i]
																				# swapping if elements not in order
																				__lowerCAmelCase	   =False
					return input_list


if __name__ == "__main__":
				print('''Enter list to be sorted''')
				lowercase_	       =			[int(x) for x in input().split()]
				# inputing elements of the list in one line
				lowercase_	       =			odd_even_sort(input_list)
				print('''The sorted list is''')
				print(sorted_list)




