


'''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 ConditionalDetrImageProcessor




class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=7 ,   lowerCamelCase_=3 ,   lowerCamelCase_=30 ,   lowerCamelCase_=4_00 ,   lowerCamelCase_=True ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   lowerCamelCase_=[0.5, 0.5, 0.5] ,   lowerCamelCase_=[0.5, 0.5, 0.5] ,   lowerCamelCase_=True ,   lowerCamelCase_=1 / 2_55 ,   lowerCamelCase_=True ,   )	->							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   __SCREAMING_SNAKE_CASE							(		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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=False       )	->							int:
														if not batched:
																					lowerCAmelCase__	       =     image_inputs[0]
																					if isinstance(__lowercase ,   Image.Image       ):
																												lowerCAmelCase__	       =     image.size
																					else:
																												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__	       =     self.get_expected_values([image]       )
																												expected_values.append((expected_height, expected_width)       )
																					lowerCAmelCase__	       =     max(__lowercase ,   key=lambda lowerCamelCase_       : item[0]       )[0]
																					lowerCAmelCase__	       =     max(__lowercase ,   key=lambda lowerCamelCase_       : item[1]       )[1]

														return expected_height, expected_width




@require_torch
@require_vision
class 		a__    (							__lowerCamelCase    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				ConditionalDetrImageProcessor if is_vision_available() else None







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     ConditionalDetrImageProcessingTester(self       )







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return self.image_processor_tester.prepare_image_processor_dict()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														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 ,   __lowercase       )

														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														# 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=__lowercase       )
														for image in image_inputs:
																					self.assertIsInstance(__lowercase ,   Image.Image       )

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

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

														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														# 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=__lowercase ,   numpify=__lowercase       )
														for image in image_inputs:
																					self.assertIsInstance(__lowercase ,   np.ndarray       )

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

														lowerCAmelCase__	       =     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
														lowerCAmelCase__	       =     image_processing(__lowercase ,   return_tensors='''pt'''       ).pixel_values

														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														# 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=__lowercase ,   torchify=__lowercase       )
														for image in image_inputs:
																					self.assertIsInstance(__lowercase ,   torch.Tensor       )

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

														lowerCAmelCase__	       =     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
														lowerCAmelCase__	       =     image_processing(__lowercase ,   return_tensors='''pt'''       ).pixel_values

														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														# 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=__lowercase ,   annotations=__lowercase ,   return_tensors='''pt'''       )

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

														lowerCAmelCase__	       =     torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
														self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,   __lowercase ,   atol=1e-4       )       )

														# verify area
														lowerCAmelCase__	       =     torch.tensor([5_887.9_600, 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'''] ,   __lowercase       )       )
														# verify boxes
														lowerCAmelCase__	       =     torch.Size([6, 4]       )
														self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   __lowercase       )
														lowerCAmelCase__	       =     torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   __lowercase ,   atol=1e-3       )       )
														# verify image_id
														lowerCAmelCase__	       =     torch.tensor([3_97_69]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   __lowercase       )       )
														# verify is_crowd
														lowerCAmelCase__	       =     torch.tensor([0, 0, 0, 0, 0, 0]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   __lowercase       )       )
														# verify class_labels
														lowerCAmelCase__	       =     torch.tensor([75, 75, 63, 65, 17, 17]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   __lowercase       )       )
														# verify orig_size
														lowerCAmelCase__	       =     torch.tensor([4_80, 6_40]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   __lowercase       )       )
														# verify size
														lowerCAmelCase__	       =     torch.tensor([8_00, 10_66]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   __lowercase       )       )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														# 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=__lowercase ,   annotations=__lowercase ,   masks_path=__lowercase ,   return_tensors='''pt'''       )

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

														lowerCAmelCase__	       =     torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
														self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,   __lowercase ,   atol=1e-4       )       )

														# verify area
														lowerCAmelCase__	       =     torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5_879.6_562, 7_634.1_147]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   __lowercase       )       )
														# verify boxes
														lowerCAmelCase__	       =     torch.Size([6, 4]       )
														self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   __lowercase       )
														lowerCAmelCase__	       =     torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   __lowercase ,   atol=1e-3       )       )
														# verify image_id
														lowerCAmelCase__	       =     torch.tensor([3_97_69]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   __lowercase       )       )
														# verify is_crowd
														lowerCAmelCase__	       =     torch.tensor([0, 0, 0, 0, 0, 0]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   __lowercase       )       )
														# verify class_labels
														lowerCAmelCase__	       =     torch.tensor([17, 17, 63, 75, 75, 93]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   __lowercase       )       )
														# verify masks
														lowerCAmelCase__	       =     82_28_73
														self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   __lowercase       )
														# verify orig_size
														lowerCAmelCase__	       =     torch.tensor([4_80, 6_40]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   __lowercase       )       )
														# verify size
														lowerCAmelCase__	       =     torch.tensor([8_00, 10_66]       )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   __lowercase       )       )



'''simple docstring'''






from __future__ import annotations


def  _snake_case       (					A			,   A	)						->   float:
							lowerCAmelCase__	       =     sorted(numsa + numsa	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     divmod(len(A	)			,   2	)
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__UpperCAmelCase       =							[float(x) for x in input('''Enter the elements of first array: ''').split()]
		__UpperCAmelCase       =							[float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")