

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 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def __init__(     self :				str							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				int=7							,   _UpperCamelCase :				Optional[int]=3							,   _UpperCamelCase :				List[Any]=1_8							,   _UpperCamelCase :				List[str]=3_0							,   _UpperCamelCase :				Optional[int]=4_0_0							,   _UpperCamelCase :				Union[str, Any]=True							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				int=True							,   )      ->Optional[Any]:
									snake_case_   =	size if size is not None else {'''height''': 1_8, '''width''': 1_8}
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	num_channels
									snake_case_   =	image_size
									snake_case_   =	min_resolution
									snake_case_   =	max_resolution
									snake_case_   =	do_resize
									snake_case_   =	size
									snake_case_   =	apply_ocr







			def 				snake_case__(     self :				List[str]							)      ->str:
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 	snake_case_   (		__A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				LayoutLMvaImageProcessor if is_pytesseract_available() else None







			def 				snake_case__(     self :				Any							)      ->int:
									snake_case_   =	LayoutLMvaImageProcessingTester(self							)







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







			def 				snake_case__(     self :				Optional[int]							)      ->Tuple:
									snake_case_   =	self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(_UpperCamelCase							,   '''do_resize'''							)							)
									self.assertTrue(hasattr(_UpperCamelCase							,   '''size'''							)							)
									self.assertTrue(hasattr(_UpperCamelCase							,   '''apply_ocr'''							)							)







			def 				snake_case__(     self :				Dict							)      ->Tuple:
									snake_case_   =	self.image_processing_class.from_dict(self.image_processor_dict							)
									self.assertEqual(image_processor.size							,   {'''height''': 1_8, '''width''': 1_8}							)

									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 				snake_case__(     self :				Dict							)      ->List[str]:
									pass







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

									# Test not batched input
									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							,   _UpperCamelCase							)
									self.assertIsInstance(encoding.boxes							,   _UpperCamelCase							)

									# Test batched
									snake_case_   =	image_processing(_UpperCamelCase							,   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 :				str							)      ->Tuple:
									# Initialize image_processing
									snake_case_   =	self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									snake_case_   =	prepare_image_inputs(self.image_processor_tester							,   equal_resolution=_UpperCamelCase							,   numpify=_UpperCamelCase							)
									for image in image_inputs:
															self.assertIsInstance(_UpperCamelCase							,   np.ndarray							)

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

									# Test not batched input
									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
									snake_case_   =	image_processing(_UpperCamelCase							,   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[int]							)      ->str:
									# with apply_OCR = True
									snake_case_   =	LayoutLMvaImageProcessor()

									from datasets import load_dataset

									snake_case_   =	load_dataset('''hf-internal-testing/fixtures_docvqa'''							,   split='''test'''							)

									snake_case_   =	Image.open(ds[0]['''file''']							).convert('''RGB'''							)

									snake_case_   =	image_processing(_UpperCamelCase							,   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_   =	[['''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_   =	[[[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							,   _UpperCamelCase							)
									self.assertListEqual(encoding.boxes							,   _UpperCamelCase							)

									# with apply_OCR = False
									snake_case_   =	LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase							)

									snake_case_   =	image_processing(_UpperCamelCase							,   return_tensors='''pt'''							)

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


from __future__ import annotations

from collections.abc import Generator


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{}
						snake_case_   =	2
						while True:
												snake_case_   =	factor_map.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												if factor:
																		snake_case_   =	factor + prime
																		while x in factor_map:
																								x += factor
																		snake_case_   =	factor
												else:
																		snake_case_   =	prime
																		yield prime
												prime += 1


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 1E10       ):
						snake_case_   =	sieve()

						snake_case_   =	1
						while True:
												snake_case_   =	next(SCREAMING_SNAKE_CASE__       )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(SCREAMING_SNAKE_CASE__       )
												n += 2


if __name__ == "__main__":
					print(solution())