


'''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 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Optional[Any]	,_a :       Dict=7	,_a :       Dict=3	,_a :       Optional[int]=18	,_a :       List[str]=30	,_a :       Union[str, Any]=400	,_a :       Optional[Any]=True	,_a :       List[str]=None	,_a :       Any=True	,):

        '''simple docstring'''




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






  def        _a							(							self :       Dict ):

        '''simple docstring'''




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







@require_torch
@require_pytesseract
class 	__lowerCAmelCase		(							SCREAMING_SNAKE_CASE__  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							LayoutLMvaImageProcessor if is_pytesseract_available() else None






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Any          =				LayoutLMvaImageProcessingTester(self )






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(_lowercase	,"""do_resize""" ) )
        self.assertTrue(hasattr(_lowercase	,"""size""" ) )
        self.assertTrue(hasattr(_lowercase	,"""apply_ocr""" ) )






  def        _a							(							self :       int ):

        '''simple docstring'''




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

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






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

        '''simple docstring'''




        pass






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        A_						:  Optional[int]          =				prepare_image_inputs(self.image_processor_tester	,equal_resolution=_lowercase )
        for image in image_inputs:
              self.assertIsInstance(_lowercase	,Image.Image )

        # Test not batched input
        A_						:  Optional[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	,_lowercase )
        self.assertIsInstance(encoding.boxes	,_lowercase )

        # Test batched
        A_						:  Union[str, Any]          =				image_processing(_lowercase	,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        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  int          =				self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        A_						:  Dict          =				prepare_image_inputs(self.image_processor_tester	,equal_resolution=_lowercase	,numpify=_lowercase )
        for image in image_inputs:
              self.assertIsInstance(_lowercase	,np.ndarray )

        # Test not batched input
        A_						:  Tuple          =				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_						:  Union[str, Any]          =				image_processing(_lowercase	,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        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        A_						:  List[Any]          =				prepare_image_inputs(self.image_processor_tester	,equal_resolution=_lowercase	,torchify=_lowercase )
        for image in image_inputs:
              self.assertIsInstance(_lowercase	,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_						:  Dict          =				image_processing(_lowercase	,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        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				LayoutLMvaImageProcessor()

        from datasets import load_dataset

        A_						:  Dict          =				load_dataset("""hf-internal-testing/fixtures_docvqa"""	,split="""test""" )

        A_						:  Any          =				Image.open(ds[0]["""file"""] ).convert("""RGB""" )

        A_						:  List[str]          =				image_processing(_lowercase	,return_tensors="""pt""" )

        self.assertEqual(encoding.pixel_values.shape	,(1, 3, 224, 224) )
        self.assertEqual(len(encoding.words )	,len(encoding.boxes ) )

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        A_						:  Optional[int]          =				[["""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_						:  int          =				[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
        # fmt: on

        self.assertListEqual(encoding.words	,_lowercase )
        self.assertListEqual(encoding.boxes	,_lowercase )

        # with apply_OCR = False
        A_						:  int          =				LayoutLMvaImageProcessor(apply_ocr=_lowercase )

        A_						:  int          =				image_processing(_lowercase	,return_tensors="""pt""" )

        self.assertEqual(encoding.pixel_values.shape	,(1, 3, 224, 224) )




'''simple docstring'''


import baseaa




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return baseaa.aaaencode(string.encode("""utf-8"""))




def      lowerCamelCase					(      lowerCamelCase  :      bytes):
      return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")


if __name__ == "__main__":
     import doctest

     doctest.testmod()
