
'''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 DetrImageProcessor




class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=7   ,       _lowercase=3   ,       _lowercase=30   ,       _lowercase=400   ,       _lowercase=True   ,       _lowercase=None   ,       _lowercase=True   ,       _lowercase=1 / 255   ,       _lowercase=True   ,       _lowercase=[0.5, 0.5, 0.5]   ,       _lowercase=[0.5, 0.5, 0.5]   ,       _lowercase=True   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			min_resolution
												_lowerCAmelCase  						=			max_resolution
												_lowerCAmelCase  						=			do_resize
												_lowerCAmelCase  						=			size
												_lowerCAmelCase  						=			do_rescale
												_lowerCAmelCase  						=			rescale_factor
												_lowerCAmelCase  						=			do_normalize
												_lowerCAmelCase  						=			image_mean
												_lowerCAmelCase  						=			image_std
												_lowerCAmelCase  						=			do_pad




						def  _lowercase     ( self				):

												"""simple docstring"""



												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_pad": self.do_pad,
												}




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=False				):

												"""simple docstring"""



												if not batched:
																		_lowerCAmelCase  						=			image_inputs[0]
																		if isinstance(_lowercase   ,       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(_lowercase   ,       key=lambda _lowercase				: item[0]				)[0]
																		_lowerCAmelCase  						=			max(_lowercase   ,       key=lambda _lowercase				: item[1]				)[1]

												return expected_height, expected_width






@require_torch
@require_vision
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				str									=       DetrImageProcessor if is_vision_available() else None




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			DetrImageProcessingTester(self				)




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_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_rescale"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """rescale_factor"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """do_resize"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """size"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """do_pad"""				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class.from_dict(self.image_processor_dict				)
												self.assertEqual(image_processor.size   ,       {"""shortest_edge""": 18, """longest_edge""": 1_333}				)
												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  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_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						,   _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						,   _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  _lowercase     ( self				):

												"""simple docstring"""



												_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						,   _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						,   _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  _lowercase     ( self				):

												"""simple docstring"""



												_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						,   _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						,   _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  _lowercase     ( self				):

												"""simple docstring"""



												_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""": 39_769, """annotations""": target}

												# encode them
												_lowerCAmelCase  						=			DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""				)
												_lowerCAmelCase  						=			image_processing(images=_lowercase   ,       annotations=_lowercase   ,       return_tensors="""pt"""				)

												# verify pixel values
												_lowerCAmelCase  						=			torch.Size([1, 3, 800, 1_066]				)
												self.assertEqual(encoding["""pixel_values"""].shape   ,       _lowercase				)

												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			torch.tensor([5887.9600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]				)
												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([0.5503, 0.2765, 0.0604, 0.2215]				)
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,       _lowercase   ,       atol=1e-3				)				)
												# verify image_id
												_lowerCAmelCase  						=			torch.tensor([39_769]				)
												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([480, 640]				)
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,       _lowercase				)				)
												# verify size
												_lowerCAmelCase  						=			torch.tensor([800, 1_066]				)
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,       _lowercase				)				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_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""": 39_769, """segments_info""": target}

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

												# encode them
												_lowerCAmelCase  						=			DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""				)
												_lowerCAmelCase  						=			image_processing(images=_lowercase   ,       annotations=_lowercase   ,       masks_path=_lowercase   ,       return_tensors="""pt"""				)

												# verify pixel values
												_lowerCAmelCase  						=			torch.Size([1, 3, 800, 1_066]				)
												self.assertEqual(encoding["""pixel_values"""].shape   ,       _lowercase				)

												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]				)
												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([0.2625, 0.5437, 0.4688, 0.8625]				)
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,       _lowercase   ,       atol=1e-3				)				)
												# verify image_id
												_lowerCAmelCase  						=			torch.tensor([39_769]				)
												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  						=			822_873
												self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,       _lowercase				)
												# verify orig_size
												_lowerCAmelCase  						=			torch.tensor([480, 640]				)
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,       _lowercase				)				)
												# verify size
												_lowerCAmelCase  						=			torch.tensor([800, 1_066]				)
												self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,       _lowercase				)				)


'''simple docstring'''



from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase    =		10


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						for i in range(__lowerCamelCase			,     __lowerCamelCase		):
												if array[i] == target:
																		return i
						return -1


def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			len(__lowerCamelCase		)
						while left <= right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		_lowerCAmelCase  						=			one_third - 1
												elif array[two_third] < target:
																		_lowerCAmelCase  						=			two_third + 1

												else:
																		_lowerCAmelCase  						=			one_third + 1
																		_lowerCAmelCase  						=			two_third - 1
						else:
												return -1


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:list[int]			,     __lowerCamelCase				:int		):
						if left < right:
												if right - left < precision:
																		return lin_search(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												_lowerCAmelCase  						=			(left + right) // 3 + 1
												_lowerCAmelCase  						=			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		return rec_ternary_search(__lowerCamelCase			,     one_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
												elif array[two_third] < target:
																		return rec_ternary_search(two_third + 1			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												else:
																		return rec_ternary_search(one_third + 1			,     two_third - 1			,     __lowerCamelCase			,     __lowerCamelCase		)
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_lowercase    =		input("""Enter numbers separated by comma:\n""").strip()
						_lowercase    =		[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), F"List must be ordered.\n{collection}."
						_lowercase    =		int(input("""Enter the number to be found in the list:\n""").strip())
						_lowercase    =		ite_ternary_search(collection, target)
						_lowercase    =		rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(F"""Iterative search: {target} found at positions: {resulta}""")
												print(F"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")
