



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 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def __init__(	self      :			str	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[Any]=7	,	__lowerCamelCase      :			Any=3	,	__lowerCamelCase      :			List[Any]=18	,	__lowerCamelCase      :			str=30	,	__lowerCamelCase      :			Any=4_00	,	__lowerCamelCase      :			Optional[Any]=True	,	__lowerCamelCase      :			Optional[Any]=None	,	__lowerCamelCase      :			Tuple=True	,	)      ->				int:
									A							:					int              =						size if size is not None else {"height": 18, "width": 18}
									A							:					List[Any]              =						parent
									A							:					int              =						batch_size
									A							:					Tuple              =						num_channels
									A							:					Optional[Any]              =						image_size
									A							:					Tuple              =						min_resolution
									A							:					Tuple              =						max_resolution
									A							:					Any              =						do_resize
									A							:					Dict              =						size
									A							:					str              =						apply_ocr







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 	lowerCamelCase_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  LayoutLMvaImageProcessor if is_pytesseract_available() else None







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									A							:					Tuple              =						LayoutLMvaImageProcessingTester(self )







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				str:
									A							:					str              =						self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(__lowerCamelCase	,	"do_resize" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"size" ) )
									self.assertTrue(hasattr(__lowerCamelCase	,	"apply_ocr" ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									A							:					Dict              =						self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size	,	{"height": 18, "width": 18} )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[int]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[Any]:
									# Initialize image_processing
									A							:					int              =						self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									A							:					List[Any]              =						prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__lowerCamelCase )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase	,	Image.Image )

									# Test not batched input
									A							:					Optional[int]              =						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	,	__lowerCamelCase )
									self.assertIsInstance(encoding.boxes	,	__lowerCamelCase )

									# Test batched
									A							:					Tuple              =						image_processing(__lowerCamelCase	,	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 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									# Initialize image_processing
									A							:					Optional[Any]              =						self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									A							:					List[str]              =						prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__lowerCamelCase	,	numpify=__lowerCamelCase )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase	,	np.ndarray )

									# Test not batched input
									A							:					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
									A							:					List[str]              =						image_processing(__lowerCamelCase	,	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 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									# Initialize image_processing
									A							:					List[str]              =						self.image_processing_class(**self.image_processor_dict )
									# create random PyTorch tensors
									A							:					Optional[Any]              =						prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__lowerCamelCase	,	torchify=__lowerCamelCase )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase	,	torch.Tensor )

									# Test not batched input
									A							:					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
									A							:					Tuple              =						image_processing(__lowerCamelCase	,	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 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Dict:
									# with apply_OCR = True
									A							:					List[Any]              =						LayoutLMvaImageProcessor()

									from datasets import load_dataset

									A							:					Optional[int]              =						load_dataset("hf-internal-testing/fixtures_docvqa"	,	split="test" )

									A							:					int              =						Image.open(ds[0]["file"] ).convert("RGB" )

									A							:					str              =						image_processing(__lowerCamelCase	,	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
									A							:					Dict              =						[["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
									A							:					Dict              =						[[[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	,	__lowerCamelCase )
									self.assertListEqual(encoding.boxes	,	__lowerCamelCase )

									# with apply_OCR = False
									A							:					Tuple              =						LayoutLMvaImageProcessor(apply_ocr=__lowerCamelCase )

									A							:					Optional[int]              =						image_processing(__lowerCamelCase	,	return_tensors="pt" )

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




from collections.abc import Sequence




def 	UpperCAmelCase   (						_lowerCamelCase = None    ):
			if nums is None or not nums:
						raise ValueError("Input sequence should not be empty"    )

			A							:					Dict              =						nums[0]
			for i in range(1 ,    len(_lowerCamelCase    )    ):
						A							:					Tuple              =						nums[i]
						A							:					List[Any]              =						max(_lowerCamelCase ,    ans + num ,    _lowerCamelCase    )

			return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							__SCREAMING_SNAKE_CASE           =  int(input("""Enter number of elements : """).strip())
							__SCREAMING_SNAKE_CASE           =  list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
							print(max_subsequence_sum(array))