



def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) < 2:
				return collection

		def circle_sort_util(_UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int						) -> bool:
				__UpperCAmelCase    :     List[Any]													=	False

				if low == high:
						return swapped

				__UpperCAmelCase    :     Any													=	low
				__UpperCAmelCase    :     Dict													=	high

				while left < right:
						if collection[left] > collection[right]:
								__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	(
								    collection[right],
								    collection[left],
								)
								__UpperCAmelCase    :     Union[str, Any]													=	True

						left += 1
						right -= 1

				if left == right and collection[left] > collection[right + 1]:
						__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	(
						    collection[right + 1],
						    collection[left],
						)

						__UpperCAmelCase    :     List[Any]													=	True

				__UpperCAmelCase    :     List[str]													=	low + int((high - low) / 2						)
				__UpperCAmelCase    :     Dict													=	circle_sort_util(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Any													=	circle_sort_util(_UpperCAmelCase ,   mid + 1 ,   _UpperCAmelCase						)

				return swapped or left_swap or right_swap

		__UpperCAmelCase    :     Union[str, Any]													=	True

		while is_not_sorted is True:
				__UpperCAmelCase    :     List[Any]													=	circle_sort_util(_UpperCAmelCase ,   0 ,   len(_UpperCAmelCase						) - 1						)

		return collection


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










import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_pytesseract_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor

class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			def __init__(   self							: Dict       ,       a_							: Dict       ,       a_							: Union[str, Any]=7       ,       a_							: Optional[Any]=3       ,       a_							: List[str]=18       ,       a_							: Union[str, Any]=30       ,       a_							: Union[str, Any]=4_00       ,       a_							: Union[str, Any]=True       ,       a_							: Tuple=None       ,       a_							: Optional[int]=True       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	size if size is not None else {'''height''': 18, '''width''': 18}
					__UpperCAmelCase    :     Dict													=	parent
					__UpperCAmelCase    :     str													=	batch_size
					__UpperCAmelCase    :     List[str]													=	num_channels
					__UpperCAmelCase    :     Union[str, Any]													=	image_size
					__UpperCAmelCase    :     Optional[int]													=	min_resolution
					__UpperCAmelCase    :     Union[str, Any]													=	max_resolution
					__UpperCAmelCase    :     Tuple													=	do_resize
					__UpperCAmelCase    :     List[str]													=	size
					__UpperCAmelCase    :     List[Any]													=	apply_ocr




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}

@require_torch
@require_pytesseract
class 		UpperCAmelCase__			(			__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       LayoutLMvaImageProcessor if is_pytesseract_available() else None




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	LayoutLMvaImageProcessingTester(self )




			@property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return self.image_processor_tester.prepare_image_processor_dict()




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(a_       ,       '''do_resize''' ) )
					self.assertTrue(hasattr(a_       ,       '''size''' ) )
					self.assertTrue(hasattr(a_       ,       '''apply_ocr''' ) )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size       ,       {'''height''': 18, '''width''': 18} )

					__UpperCAmelCase    :     int													=	self.image_processing_class.from_dict(self.image_processor_dict       ,       size=42 )
					self.assertEqual(image_processor.size       ,       {'''height''': 42, '''width''': 42} )




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					pass




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					__UpperCAmelCase    :     Dict													=	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    :     List[Any]													=	image_processing(image_inputs[0]       ,       return_tensors='''pt''' )
					self.assertEqual(
					    encoding.pixel_values.shape       ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )

					self.assertIsInstance(encoding.words       ,       a_ )
					self.assertIsInstance(encoding.boxes       ,       a_ )

					# Test batched
					__UpperCAmelCase    :     Optional[Any]													=	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,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					__UpperCAmelCase    :     Optional[Any]													=	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    :     Dict													=	image_processing(image_inputs[0]       ,       return_tensors='''pt''' ).pixel_values
					self.assertEqual(
					    encoded_images.shape       ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )

					# Test batched
					__UpperCAmelCase    :     int													=	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,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					__UpperCAmelCase    :     Tuple													=	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    :     str													=	image_processing(image_inputs[0]       ,       return_tensors='''pt''' ).pixel_values
					self.assertEqual(
					    encoded_images.shape       ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )

					# Test batched
					__UpperCAmelCase    :     List[Any]													=	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,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,       )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	LayoutLMvaImageProcessor()

					from datasets import load_dataset

					__UpperCAmelCase    :     Any													=	load_dataset('''hf-internal-testing/fixtures_docvqa'''       ,       split='''test''' )

					__UpperCAmelCase    :     Optional[int]													=	Image.open(ds[0]['''file'''] ).convert('''RGB''' )

					__UpperCAmelCase    :     Any													=	image_processing(a_       ,       return_tensors='''pt''' )

					self.assertEqual(encoding.pixel_values.shape       ,       (1, 3, 2_24, 2_24) )
					self.assertEqual(len(encoding.words )       ,       len(encoding.boxes ) )

					# fmt: off
					# the words and boxes were obtained with Tesseract 4.1.1
					__UpperCAmelCase    :     Any													=	[['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
					__UpperCAmelCase    :     Tuple													=	[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
					# fmt: on

					self.assertListEqual(encoding.words       ,       a_ )
					self.assertListEqual(encoding.boxes       ,       a_ )

					# with apply_OCR = False
					__UpperCAmelCase    :     List[str]													=	LayoutLMvaImageProcessor(apply_ocr=a_ )

					__UpperCAmelCase    :     List[Any]													=	image_processing(a_       ,       return_tensors='''pt''' )

					self.assertEqual(encoding.pixel_values.shape       ,       (1, 3, 2_24, 2_24) )





