






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     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					List[Any]=7 ,  __lowerCamelCase				:					Any=3 ,  __lowerCamelCase				:					Optional[int]=18 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					List[str]=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					Optional[Any]=None ,  __lowerCamelCase				:					int=True ,  ):
		UpperCamelCase     :Optional[int]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :Optional[int]          =	parent
		UpperCamelCase     :str          =	batch_size
		UpperCamelCase     :List[Any]          =	num_channels
		UpperCamelCase     :List[str]          =	image_size
		UpperCamelCase     :List[Any]          =	min_resolution
		UpperCamelCase     :List[Any]          =	max_resolution
		UpperCamelCase     :Any          =	do_resize
		UpperCamelCase     :Dict          =	size
		UpperCamelCase     :Dict          =	apply_ocr





	def 						_A (		self				:					Optional[int]	):
		return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}

@require_torch
@require_pytesseract
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  LayoutLMvaImageProcessor if is_pytesseract_available() else None





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :Any          =	LayoutLMvaImageProcessingTester(self	)





	@property
	def 						_A (		self				:					str	):
		return self.image_processor_tester.prepare_image_processor_dict()





	def 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :Union[str, Any]          =	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 						_A (		self				:					List[str]	):
		UpperCamelCase     :List[str]          =	self.image_processing_class.from_dict(self.image_processor_dict	)
		self.assertEqual(image_processor.size ,  {"""height""": 18, """width""": 18}	)

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





	def 						_A (		self				:					List[Any]	):
		pass





	def 						_A (		self				:					Any	):
		# Initialize image_processing
		UpperCamelCase     :Any          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :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
		UpperCamelCase     :Tuple          =	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
		UpperCamelCase     :Optional[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 						_A (		self				:					Dict	):
		# Initialize image_processing
		UpperCamelCase     :Optional[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :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
		UpperCamelCase     :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
		UpperCamelCase     :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 						_A (		self				:					int	):
		# Initialize image_processing
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :int          =	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
		UpperCamelCase     :Union[str, Any]          =	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
		UpperCamelCase     :Dict          =	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 						_A (		self				:					List[Any]	):
		# with apply_OCR = True
		UpperCamelCase     :Optional[Any]          =	LayoutLMvaImageProcessor()

		from datasets import load_dataset

		UpperCamelCase     :int          =	load_dataset("""hf-internal-testing/fixtures_docvqa""" ,  split="""test"""	)

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

		UpperCamelCase     :Union[str, Any]          =	image_processing(__lowerCamelCase ,  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
		UpperCamelCase     :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
		UpperCamelCase     :List[str]          =	[[[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 ,  __lowerCamelCase	)
		self.assertListEqual(encoding.boxes ,  __lowerCamelCase	)

		# with apply_OCR = False
		UpperCamelCase     :List[Any]          =	LayoutLMvaImageProcessor(apply_ocr=__lowerCamelCase	)

		UpperCamelCase     :Tuple          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	)

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)