



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 			_snake_case     (   unittest.TestCase):




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




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

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

							return expected_height, expected_width
@require_torch
@require_vision
class 			_snake_case     (   lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   List[str]       						=ConditionalDetrImageProcessor if is_vision_available() else None




				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__   					=			ConditionalDetrImageProcessingTester(self  )




				@property
				def 							A__ (  self   :  Tuple  ):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__ (  self   :  List[Any]  ):
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(__lowercase,   "image_mean"  )  )
							self.assertTrue(hasattr(__lowercase,   "image_std"  )  )
							self.assertTrue(hasattr(__lowercase,   "do_normalize"  )  )
							self.assertTrue(hasattr(__lowercase,   "do_resize"  )  )
							self.assertTrue(hasattr(__lowercase,   "size"  )  )




				def 							A__ (  self   :  int  ):
							lowercase__   					=			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,   __lowercase  )

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




				def 							A__ (  self   :  Tuple  ):
							pass




				def 							A__ (  self   :  Optional[int]  ):
							# Initialize image_processing
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowercase__   					=			prepare_image_inputs(self.image_processor_tester,   equal_resolution=__lowercase  )
							for image in image_inputs:
										self.assertIsInstance(__lowercase,   Image.Image  )

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

							lowercase__					,    lowercase__   					=			self.image_processor_tester.get_expected_values(__lowercase  )

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

							# Test batched
							lowercase__					,    lowercase__   					=			self.image_processor_tester.get_expected_values(__lowercase,   batched=__lowercase  )

							lowercase__   					=			image_processing(__lowercase,   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   :  str  ):
							# Initialize image_processing
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowercase__   					=			prepare_image_inputs(self.image_processor_tester,   equal_resolution=__lowercase,   numpify=__lowercase  )
							for image in image_inputs:
										self.assertIsInstance(__lowercase,   np.ndarray  )

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

							lowercase__					,    lowercase__   					=			self.image_processor_tester.get_expected_values(__lowercase  )

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

							# Test batched
							lowercase__   					=			image_processing(__lowercase,   return_tensors="pt"  ).pixel_values

							lowercase__					,    lowercase__   					=			self.image_processor_tester.get_expected_values(__lowercase,   batched=__lowercase  )

							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]  ):
							# Initialize image_processing
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowercase__   					=			prepare_image_inputs(self.image_processor_tester,   equal_resolution=__lowercase,   torchify=__lowercase  )
							for image in image_inputs:
										self.assertIsInstance(__lowercase,   torch.Tensor  )

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

							lowercase__					,    lowercase__   					=			self.image_processor_tester.get_expected_values(__lowercase  )

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

							# Test batched
							lowercase__   					=			image_processing(__lowercase,   return_tensors="pt"  ).pixel_values

							lowercase__					,    lowercase__   					=			self.image_processor_tester.get_expected_values(__lowercase,   batched=__lowercase  )

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




				@slow
				def 							A__ (  self   :  List[str]  ):
							# prepare image and target
							lowercase__   					=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt",   "r"  ) as f:
										lowercase__   					=			json.loads(f.read()  )

							lowercase__   					=			{"image_id": 3_9769, "annotations": target}

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

							# verify pixel values
							lowercase__   					=			torch.Size([1, 3, 800, 1066]  )
							self.assertEqual(encoding["pixel_values"].shape,   __lowercase  )

							lowercase__   					=			torch.tensor([0.2796, 0.3138, 0.3481]  )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],   __lowercase,   atol=1e-4  )  )

							# verify area
							lowercase__   					=			torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"],   __lowercase  )  )
							# verify boxes
							lowercase__   					=			torch.Size([6, 4]  )
							self.assertEqual(encoding["labels"][0]["boxes"].shape,   __lowercase  )
							lowercase__   					=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],   __lowercase,   atol=1e-3  )  )
							# verify image_id
							lowercase__   					=			torch.tensor([3_9769]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],   __lowercase  )  )
							# verify is_crowd
							lowercase__   					=			torch.tensor([0, 0, 0, 0, 0, 0]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],   __lowercase  )  )
							# verify class_labels
							lowercase__   					=			torch.tensor([75, 75, 63, 65, 17, 17]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],   __lowercase  )  )
							# verify orig_size
							lowercase__   					=			torch.tensor([480, 640]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],   __lowercase  )  )
							# verify size
							lowercase__   					=			torch.tensor([800, 1066]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"],   __lowercase  )  )




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

							lowercase__   					=			{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

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

							# verify pixel values
							lowercase__   					=			torch.Size([1, 3, 800, 1066]  )
							self.assertEqual(encoding["pixel_values"].shape,   __lowercase  )

							lowercase__   					=			torch.tensor([0.2796, 0.3138, 0.3481]  )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],   __lowercase,   atol=1e-4  )  )

							# verify area
							lowercase__   					=			torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"],   __lowercase  )  )
							# verify boxes
							lowercase__   					=			torch.Size([6, 4]  )
							self.assertEqual(encoding["labels"][0]["boxes"].shape,   __lowercase  )
							lowercase__   					=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],   __lowercase,   atol=1e-3  )  )
							# verify image_id
							lowercase__   					=			torch.tensor([3_9769]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],   __lowercase  )  )
							# verify is_crowd
							lowercase__   					=			torch.tensor([0, 0, 0, 0, 0, 0]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],   __lowercase  )  )
							# verify class_labels
							lowercase__   					=			torch.tensor([17, 17, 63, 75, 75, 93]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],   __lowercase  )  )
							# verify masks
							lowercase__   					=			82_2873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item(),   __lowercase  )
							# verify orig_size
							lowercase__   					=			torch.tensor([480, 640]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],   __lowercase  )  )
							# verify size
							lowercase__   					=			torch.tensor([800, 1066]  )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"],   __lowercase  )  )









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			len(SCREAMING_SNAKE_CASE_					)
			for i in range(SCREAMING_SNAKE_CASE_					):
						for j in range(i + 1  ,					SCREAMING_SNAKE_CASE_					):
									if numbers[j] < numbers[i]:
												lowercase__					,    lowercase__   					=			numbers[j], numbers[i]
			return numbers


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




