
"""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 DetaImageProcessor




class 			A__	(     unittest.TestCase):

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

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

			else:
				__lowerCAmelCase      :						List[Any]						      =				[]
				for image in image_inputs:
					__lowerCAmelCase      ,       __lowerCAmelCase      :						Tuple						      =				self.get_expected_values([image]			)
					expected_values.append((expected_height, expected_width)			)
				__lowerCAmelCase      :						Union[str, Any]						      =				max(_SCREAMING_SNAKE_CASE	,		key=lambda _SCREAMING_SNAKE_CASE			: item[0]			)[0]
				__lowerCAmelCase      :						Dict						      =				max(_SCREAMING_SNAKE_CASE	,		key=lambda _SCREAMING_SNAKE_CASE			: item[1]			)[1]

			return expected_height, expected_width




@require_torch
@require_vision
class 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	str           =     DetaImageProcessor if is_vision_available() else None

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				DetaImageProcessingTester(self			)

		@property
		def 							__lowerCamelCase							(				self			):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'image_mean'			)			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'image_std'			)			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'do_normalize'			)			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'do_resize'			)			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'do_rescale'			)			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'do_pad'			)			)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'size'			)			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				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	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			pass

		def 							__lowerCamelCase							(				self			):
			# Initialize image_processing
			__lowerCAmelCase      :						List[Any]						      =				self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			__lowerCAmelCase      :						str						      =				prepare_image_inputs(self.image_processor_tester	,		equal_resolution=_SCREAMING_SNAKE_CASE			)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE	,		Image.Image			)

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

			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			)

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

			# Test batched
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Dict						      =				self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	,		batched=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[Any]						      =				image_processing(_SCREAMING_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			):
			# Initialize image_processing
			__lowerCAmelCase      :						int						      =				self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			__lowerCAmelCase      :						int						      =				prepare_image_inputs(self.image_processor_tester	,		equal_resolution=_SCREAMING_SNAKE_CASE	,		numpify=_SCREAMING_SNAKE_CASE			)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE	,		np.ndarray			)

			# Test not batched input
			__lowerCAmelCase      :						Union[str, Any]						      =				image_processing(image_inputs[0]	,		return_tensors='pt'			).pixel_values

			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			)

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

			# Test batched
			__lowerCAmelCase      :						Any						      =				image_processing(_SCREAMING_SNAKE_CASE	,		return_tensors='pt'			).pixel_values

			__lowerCAmelCase      ,       __lowerCAmelCase      :						Tuple						      =				self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	,		batched=_SCREAMING_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			):
			# Initialize image_processing
			__lowerCAmelCase      :						int						      =				self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			__lowerCAmelCase      :						int						      =				prepare_image_inputs(self.image_processor_tester	,		equal_resolution=_SCREAMING_SNAKE_CASE	,		torchify=_SCREAMING_SNAKE_CASE			)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE	,		torch.Tensor			)

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

			__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE			)

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

			# Test batched
			__lowerCAmelCase      :						int						      =				image_processing(_SCREAMING_SNAKE_CASE	,		return_tensors='pt'			).pixel_values

			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	,		batched=_SCREAMING_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 							__lowerCamelCase							(				self			):
			# prepare image and target
			__lowerCAmelCase      :						Union[str, Any]						      =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'	,		'r'			) as f:
				__lowerCAmelCase      :						Tuple						      =				json.loads(f.read()			)

			__lowerCAmelCase      :						Tuple						      =				{'image_id': 3_97_69, 'annotations': target}

			# encode them
			__lowerCAmelCase      :						int						      =				DetaImageProcessor()
			__lowerCAmelCase      :						Optional[int]						      =				image_processing(images=_SCREAMING_SNAKE_CASE	,		annotations=_SCREAMING_SNAKE_CASE	,		return_tensors='pt'			)

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

			__lowerCAmelCase      :						Optional[Any]						      =				torch.tensor([0.2796, 0.3138, 0.3481]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]	,		_SCREAMING_SNAKE_CASE	,		atol=1E-4			)			)

			# verify area
			__lowerCAmelCase      :						Tuple						      =				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify boxes
			__lowerCAmelCase      :						int						      =				torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3			)			)
			# verify image_id
			__lowerCAmelCase      :						str						      =				torch.tensor([3_97_69]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify is_crowd
			__lowerCAmelCase      :						int						      =				torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify class_labels
			__lowerCAmelCase      :						List[Any]						      =				torch.tensor([75, 75, 63, 65, 17, 17]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify orig_size
			__lowerCAmelCase      :						Dict						      =				torch.tensor([4_80, 6_40]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify size
			__lowerCAmelCase      :						List[Any]						      =				torch.tensor([8_00, 10_66]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']	,		_SCREAMING_SNAKE_CASE			)			)







		@slow
		def 							__lowerCamelCase							(				self			):
			# prepare image, target and masks_path
			__lowerCAmelCase      :						Dict						      =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'	,		'r'			) as f:
				__lowerCAmelCase      :						Dict						      =				json.loads(f.read()			)

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

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

			# encode them
			__lowerCAmelCase      :						Union[str, Any]						      =				DetaImageProcessor(format='coco_panoptic'			)
			__lowerCAmelCase      :						List[Any]						      =				image_processing(images=_SCREAMING_SNAKE_CASE	,		annotations=_SCREAMING_SNAKE_CASE	,		masks_path=_SCREAMING_SNAKE_CASE	,		return_tensors='pt'			)

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

			__lowerCAmelCase      :						Dict						      =				torch.tensor([0.2796, 0.3138, 0.3481]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]	,		_SCREAMING_SNAKE_CASE	,		atol=1E-4			)			)

			# verify area
			__lowerCAmelCase      :						Optional[int]						      =				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify boxes
			__lowerCAmelCase      :						Optional[int]						      =				torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3			)			)
			# verify image_id
			__lowerCAmelCase      :						Dict						      =				torch.tensor([3_97_69]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify is_crowd
			__lowerCAmelCase      :						str						      =				torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify class_labels
			__lowerCAmelCase      :						List[Any]						      =				torch.tensor([17, 17, 63, 75, 75, 93]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify masks
			__lowerCAmelCase      :						List[Any]						      =				82_28_73
			self.assertEqual(encoding['labels'][0]['masks'].sum().item()	,		_SCREAMING_SNAKE_CASE			)
			# verify orig_size
			__lowerCAmelCase      :						str						      =				torch.tensor([4_80, 6_40]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']	,		_SCREAMING_SNAKE_CASE			)			)
			# verify size
			__lowerCAmelCase      :						List[str]						      =				torch.tensor([8_00, 10_66]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size']	,		_SCREAMING_SNAKE_CASE			)			)

"""simple docstring"""







def    __lowerCAmelCase    (_UpperCamelCase		):
	if len(_UpperCamelCase		) <= 1:
		return lst

	__lowerCAmelCase      :						str						      =				1

	while i < len(_UpperCamelCase		):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				__lowerCAmelCase      :						int						      =				1

	return lst


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