





'''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				:			Any      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Optional[Any]=7      ,						lowerCamelCase_				:			List[Any]=3      ,						lowerCamelCase_				:			Union[str, Any]=18      ,						lowerCamelCase_				:			int=30      ,						lowerCamelCase_				:			Optional[int]=4_00      ,						lowerCamelCase_				:			Tuple=True      ,						lowerCamelCase_				:			str=None      ,						lowerCamelCase_				:			List[str]=True      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			size if size is not None else {"""height""": 18, """width""": 18}
							SCREAMING_SNAKE_CASE :				Optional[int]		=			parent
							SCREAMING_SNAKE_CASE :				Tuple		=			batch_size
							SCREAMING_SNAKE_CASE :				Dict		=			num_channels
							SCREAMING_SNAKE_CASE :				Dict		=			image_size
							SCREAMING_SNAKE_CASE :				List[Any]		=			min_resolution
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			max_resolution
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			do_resize
							SCREAMING_SNAKE_CASE :				str		=			size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			apply_ocr



					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



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







@require_torch
@require_pytesseract
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	LayoutLMvaImageProcessor if is_pytesseract_available() else None


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			LayoutLMvaImageProcessingTester(self						)


					@property
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				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 lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size      ,						{"""height""": 18, """width""": 18}						)

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


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							SCREAMING_SNAKE_CASE :				Optional[int]		=			prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=lowerCamelCase_						)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase_      ,						Image.Image						)

							# Test not batched input
							SCREAMING_SNAKE_CASE :				Any		=			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
							SCREAMING_SNAKE_CASE :				List[Any]		=			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 lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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
							SCREAMING_SNAKE_CASE :				List[str]		=			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 :				Any		=			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 lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE :				Union[str, 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
							SCREAMING_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
							SCREAMING_SNAKE_CASE :				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 lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			LayoutLMvaImageProcessor()

							from datasets import load_dataset

							SCREAMING_SNAKE_CASE :				List[str]		=			load_dataset("""hf-internal-testing/fixtures_docvqa"""      ,						split="""test"""						)

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

							SCREAMING_SNAKE_CASE :				Any		=			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
							SCREAMING_SNAKE_CASE :				Tuple		=			[["""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 :				Tuple		=			[[[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
							SCREAMING_SNAKE_CASE :				List[Any]		=			LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			image_processing(lowerCamelCase_      ,						return_tensors="""pt"""						)

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










'''simple docstring'''




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
							from transformers import MarkupLMFeatureExtractor




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			parent



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

							'''simple docstring'''



							return {}







def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			"""<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR=\"FFFFFF\">
    <HR>
    <a href=\"http://google.com\">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style=\"color:#0000FF\">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>"""

		SCREAMING_SNAKE_CASE :				int		=			"""
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    """

		return [html_string_a, html_string_a]




@require_bsa
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	MarkupLMFeatureExtractor if is_bsa_available() else None


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			MarkupLMFeatureExtractionTester(self						)


					@property
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							return self.feature_extract_tester.prepare_feat_extract_dict()



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.feature_extraction_class()

							# Test not batched input
							SCREAMING_SNAKE_CASE :				Optional[int]		=			get_html_strings()[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			feature_extractor(lowerCamelCase_						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[["""sample document""", """Goog""", """This is one header""", """This is a another Header""", """Travel from""", """SFO to JFK""", """on May 2, 2015 at 2:00 pm. For details go to confirm.com""", """Traveler""", """name""", """is""", """John Doe"""]]
							SCREAMING_SNAKE_CASE :				List[str]		=			[["""/html/head/title""", """/html/body/a""", """/html/body/h1""", """/html/body/h2""", """/html/body/p""", """/html/body/p/p/b[1]""", """/html/body/p/p/b[2]/i""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/b""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/p"""]]
							# fmt: on

							self.assertEqual(encoding.nodes      ,						lowerCamelCase_						)
							self.assertEqual(encoding.xpaths      ,						lowerCamelCase_						)

							# Test batched
							SCREAMING_SNAKE_CASE :				str		=			get_html_strings()
							SCREAMING_SNAKE_CASE :				str		=			feature_extractor(lowerCamelCase_						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				int		=			expected_nodes + [["""My First Heading""", """My first paragraph."""]]
							SCREAMING_SNAKE_CASE :				List[Any]		=			expected_xpaths + [["""/html/body/h1""", """/html/body/p"""]]

							self.assertEqual(len(encoding.nodes						)      ,						2						)
							self.assertEqual(len(encoding.xpaths						)      ,						2						)

							self.assertEqual(encoding.nodes      ,						lowerCamelCase_						)
							self.assertEqual(encoding.xpaths      ,						lowerCamelCase_						)



