
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 DeformableDetrImageProcessor







class     __lowerCAmelCase					(    unittest.TestCase		):



			def __init__(		self     :						List[Any]				,	A     :						Tuple				,	A     :						int=7				,	A     :						str=3				,	A     :						List[Any]=30				,	A     :						str=4_00				,	A     :						Tuple=True				,	A     :						str=None				,	A     :						Tuple=True				,	A     :						List[str]=[0.5, 0.5, 0.5]				,	A     :						int=[0.5, 0.5, 0.5]				,	A     :						List[str]=True				,	A     :						List[str]=1 / 2_55				,	A     :						List[Any]=True				,	)     ->					Optional[Any]:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					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     :						Tuple				,	A     :						int				,	A     :						int=False)     ->					Dict:



						"""simple docstring"""






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

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

						return expected_height, expected_width







@require_torch
@require_vision
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  DeformableDetrImageProcessor if is_vision_available() else None



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     DeformableDetrImageProcessingTester(self)



			@property
			def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     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     :						str)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     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				,	A)

						_UpperCAmelCase	 =     self.image_processing_class.from_dict(
						    self.image_processor_dict				,	size=42				,	max_size=84				,	pad_and_return_pixel_mask=A)
						self.assertEqual(image_processor.size				,	{'shortest_edge': 42, 'longest_edge': 84})
						self.assertEqual(image_processor.do_pad				,	A)



			def 					_lowerCamelCase (		self     :						List[str])     ->					Dict:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						List[str])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase	 =     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	 =     image_processing(image_inputs[0]				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     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	 =     self.image_processor_tester.get_expected_values(A				,	batched=A)

						_UpperCAmelCase	 =     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     :						List[str])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase	 =     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	 =     image_processing(image_inputs[0]				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     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	 =     image_processing(A				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     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     :						List[str])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase	 =     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	 =     image_processing(image_inputs[0]				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     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	 =     image_processing(A				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     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     :						str)     ->					List[str]:



						"""simple docstring"""






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

						_UpperCAmelCase	 =     {'image_id': 3_97_69, 'annotations': target}

						# encode them
						_UpperCAmelCase	 =     DeformableDetrImageProcessor()
						_UpperCAmelCase	 =     image_processing(images=A				,	annotations=A				,	return_tensors='pt')

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

						_UpperCAmelCase	 =     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]				,	A				,	atol=1E-4))

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



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



						"""simple docstring"""






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

						_UpperCAmelCase	 =     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

						# encode them
						_UpperCAmelCase	 =     DeformableDetrImageProcessor(format='coco_panoptic')
						_UpperCAmelCase	 =     image_processing(images=A				,	annotations=A				,	masks_path=A				,	return_tensors='pt')

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

						_UpperCAmelCase	 =     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]				,	A				,	atol=1E-4))

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





def 							A			(  _UpperCAmelCase       :     list					)       ->				list:







			'''simple docstring'''
			if len(_UpperCAmelCase					) <= 1:
						return lst

			_UpperCAmelCase	 =     1

			while i < len(_UpperCAmelCase					):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_UpperCAmelCase	 =     1

			return lst


if __name__ == "__main__":
							UpperCAmelCase__     = input("Enter numbers separated by a comma:\n").strip()
							UpperCAmelCase__     = [int(item) for item in user_input.split(",")]
							print(gnome_sort(unsorted))
