





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       ):






					"""simple docstring"""



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

						"""simple docstring"""



						lowerCamelCase__										=			size if size is not None else {"""height""": 18, """width""": 18}
						lowerCamelCase__										=			parent
						lowerCamelCase__										=			batch_size
						lowerCamelCase__										=			num_channels
						lowerCamelCase__										=			image_size
						lowerCamelCase__										=			min_resolution
						lowerCamelCase__										=			max_resolution
						lowerCamelCase__										=			do_resize
						lowerCamelCase__										=			size
						lowerCamelCase__										=			apply_ocr





					def       _UpperCamelCase       (    self						:     Union[str, Any]							):

						"""simple docstring"""



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






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






					"""simple docstring"""



					snake_case_						  =			LayoutLMvaImageProcessor if is_pytesseract_available() else None





					def       _UpperCamelCase       (    self						:     Optional[int]							):

						"""simple docstring"""



						lowerCamelCase__										=			LayoutLMvaImageProcessingTester(self							)





					@property
					def       _UpperCamelCase       (    self						:     List[str]							):

						"""simple docstring"""



						return self.image_processor_tester.prepare_image_processor_dict()





					def       _UpperCamelCase       (    self						:     int							):

						"""simple docstring"""



						lowerCamelCase__										=			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       _UpperCamelCase       (    self						:     Optional[Any]							):

						"""simple docstring"""



						lowerCamelCase__										=			self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size ,					{"""height""": 18, """width""": 18}							)

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





					def       _UpperCamelCase       (    self						:     Any							):

						"""simple docstring"""



						pass





					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



						lowerCamelCase__										=			self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						lowerCamelCase__										=			prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__lowerCAmelCase							)
						for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase ,					Image.Image							)

						# Test not batched input
						lowerCamelCase__										=			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
						lowerCamelCase__										=			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       _UpperCamelCase       (    self						:     int							):

						"""simple docstring"""



						lowerCamelCase__										=			self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						lowerCamelCase__										=			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
						lowerCamelCase__										=			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
						lowerCamelCase__										=			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       _UpperCamelCase       (    self						:     Optional[int]							):

						"""simple docstring"""



						lowerCamelCase__										=			self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						lowerCamelCase__										=			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
						lowerCamelCase__										=			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
						lowerCamelCase__										=			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       _UpperCamelCase       (    self						:     List[Any]							):

						"""simple docstring"""



						lowerCamelCase__										=			LayoutLMvaImageProcessor()

						from datasets import load_dataset

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

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

						lowerCamelCase__										=			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
						lowerCamelCase__										=			[["""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
						lowerCamelCase__										=			[[[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
						lowerCamelCase__										=			LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase							)

						lowerCamelCase__										=			image_processing(__lowerCAmelCase ,					return_tensors="""pt"""							)

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






'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2    ,	hamming_list[j] * 3    ,	hamming_list[k] * 5							)							)
												index += 1
						return hamming_list


if __name__ == "__main__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

