






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  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=7				,			a=3				,			a=1_8				,			a=3_0				,			a=4_0_0				,			a=True				,			a=None				,			a=True				,			):
       snake_case__  :    Optional[Any]							       =size if size is not None else {"""height""": 1_8, """width""": 1_8}
       snake_case__  :    int							       =parent
       snake_case__  :    int							       =batch_size
       snake_case__  :    List[str]							       =num_channels
       snake_case__  :    List[Any]							       =image_size
       snake_case__  :    List[Any]							       =min_resolution
       snake_case__  :    str							       =max_resolution
       snake_case__  :    List[Any]							       =do_resize
       snake_case__  :    int							       =size
       snake_case__  :    str							       =apply_ocr



      def 						lowercase__					(     self   ):
       return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						int										=							LayoutLMvaImageProcessor if is_pytesseract_available() else None
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =LayoutLMvaImageProcessingTester(self   )
      @property
      def 						lowercase__					(     self   ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =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 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size				,			{"""height""": 1_8, """width""": 1_8}   )

       snake_case__  :    List[str]							       =self.image_processing_class.from_dict(self.image_processor_dict				,			size=4_2   )
       self.assertEqual(image_processor.size				,			{"""height""": 4_2, """width""": 4_2}   )
      def 						lowercase__					(     self   ):
       pass
      def 						lowercase__					(     self   ):
       # Initialize image_processing
       snake_case__  :    Tuple							       =self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       snake_case__  :    List[Any]							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=a   )
       for image in image_inputs:
        self.assertIsInstance(a				,			Image.Image   )

       # Test not batched input
       snake_case__  :    str							       =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
       snake_case__  :    List[str]							       =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 						lowercase__					(     self   ):
       # Initialize image_processing
       snake_case__  :    Any							       =self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       snake_case__  :    List[str]							       =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
       snake_case__  :    Optional[int]							       =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
       snake_case__  :    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 						lowercase__					(     self   ):
       # Initialize image_processing
       snake_case__  :    Tuple							       =self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       snake_case__  :    Optional[int]							       =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
       snake_case__  :    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
       snake_case__  :    Dict							       =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 						lowercase__					(     self   ):
       # with apply_OCR = True
       snake_case__  :    Any							       =LayoutLMvaImageProcessor()

       from datasets import load_dataset

       snake_case__  :    str							       =load_dataset("""hf-internal-testing/fixtures_docvqa"""				,			split="""test"""   )

       snake_case__  :    Any							       =Image.open(ds[0]["""file"""]   ).convert("""RGB"""   )

       snake_case__  :    Union[str, Any]							       =image_processing(a				,			return_tensors="""pt"""   )

       self.assertEqual(encoding.pixel_values.shape				,			(1, 3, 2_2_4, 2_2_4)   )
       self.assertEqual(len(encoding.words   )				,			len(encoding.boxes   )   )

       # fmt: off
       # the words and boxes were obtained with Tesseract 4.1.1
       snake_case__  :    Optional[int]							       =[["""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
       snake_case__  :    str							       =[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
       # fmt: on

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

       # with apply_OCR = False
       snake_case__  :    List[str]							       =LayoutLMvaImageProcessor(apply_ocr=a   )

       snake_case__  :    List[Any]							       =image_processing(a				,			return_tensors="""pt"""   )

       self.assertEqual(encoding.pixel_values.shape				,			(1, 3, 2_2_4, 2_2_4)   )













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





