


"""simple docstring"""







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    :       List[str]    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       str=7    ,    UpperCAmelCase__    :       Dict=3    ,    UpperCAmelCase__    :       Tuple=1_8    ,    UpperCAmelCase__    :       Optional[int]=3_0    ,    UpperCAmelCase__    :       Tuple=4_0_0    ,    UpperCAmelCase__    :       Optional[Any]=True    ,    UpperCAmelCase__    :       str=None    ,    UpperCAmelCase__    :       Dict=True    ,    ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"""height""": 1_8, """width""": 1_8}
						__SCREAMING_SNAKE_CASE		     =       parent
						__SCREAMING_SNAKE_CASE		     =       batch_size
						__SCREAMING_SNAKE_CASE		     =       num_channels
						__SCREAMING_SNAKE_CASE		     =       image_size
						__SCREAMING_SNAKE_CASE		     =       min_resolution
						__SCREAMING_SNAKE_CASE		     =       max_resolution
						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       apply_ocr




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[int]:
						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"""





	snake_case__       :						Optional[Any]					      =    LayoutLMvaImageProcessor if is_pytesseract_available() else None




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       LayoutLMvaImageProcessingTester(self     )




	@property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()




	def 						UpperCAmelCase_   (				self    :       str     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class.from_dict(self.image_processor_dict     )
						self.assertEqual(image_processor.size    ,    {"height": 1_8, "width": 1_8}     )

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




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			str:
						# Initialize image_processing
						__SCREAMING_SNAKE_CASE		     =       self.image_processing_class(**self.image_processor_dict     )
						# create random PIL images
						__SCREAMING_SNAKE_CASE		     =       prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=A__     )
						for image in image_inputs:
											self.assertIsInstance(A__    ,    Image.Image     )

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

						from datasets import load_dataset

						__SCREAMING_SNAKE_CASE		     =       load_dataset("hf-internal-testing/fixtures_docvqa"    ,    split="test"     )

						__SCREAMING_SNAKE_CASE		     =       Image.open(ds[0]["file"]     ).convert("RGB"     )

						__SCREAMING_SNAKE_CASE		     =       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
						__SCREAMING_SNAKE_CASE		     =       [["""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
						__SCREAMING_SNAKE_CASE		     =       [[[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
						__SCREAMING_SNAKE_CASE		     =       LayoutLMvaImageProcessor(apply_ocr=A__     )

						__SCREAMING_SNAKE_CASE		     =       image_processing(A__    ,    return_tensors="pt"     )

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




"""simple docstring"""







from __future__ import annotations

import math




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       int     ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       size
						# approximate the overall size of segment tree with given value
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]
						# create array to store lazy update
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]  # flag for lazy update




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2 + 1




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       list[int]     ) ->			None:
						if left_element == right_element:
											__SCREAMING_SNAKE_CASE		     =       a[left_element - 1]
						else:
											__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
											self.build(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											self.build(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       max(
											    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			bool:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True

						if right_element < a or left_element > b:
											return True
						if left_element >= a and right_element <= b:
											__SCREAMING_SNAKE_CASE		     =       val
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
											return True
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						self.update(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						self.update(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       max(
						    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )
						return True




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			int | float:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
						if right_element < a or left_element > b:
											return -math.inf
						if left_element >= a and right_element <= b:
											return self.segment_tree[idx]
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						__SCREAMING_SNAKE_CASE		     =       self.query(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.query(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						return max(UpperCAmelCase__    ,    UpperCAmelCase__     )




	def __str__(				self    :       int     ) ->			str:
						return str([self.query(1    ,    1    ,    self.size    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ) for i in range(1    ,    self.size + 1     )]     )


if __name__ == "__main__":
		a__       :							Tuple        =  [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
		a__       :							Dict        =  1_5
		a__       :							int        =  SegmentTree(size)
		segt.build(1, 1, size, A)
		print(segt.query(1, 1, size, 4, 6))
		print(segt.query(1, 1, size, 7, 1_1))
		print(segt.query(1, 1, size, 7, 1_2))
		segt.update(1, 1, size, 1, 3, 1_1_1)
		print(segt.query(1, 1, size, 1, 1_5))
		segt.update(1, 1, size, 7, 8, 2_3_5)
		print(segt)
