




"""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   a__					(						unittest.TestCase ):


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


						"""simple docstring"""




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


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




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






@require_torch
@require_pytesseract
class   a__					(						__magic_name__				,     unittest.TestCase ):
			lowercase_		=						LayoutLMvaImageProcessor if is_pytesseract_available() else None


			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       LayoutLMvaImageProcessingTester(self)


			@property
			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						return self.image_processor_tester.prepare_image_processor_dict()


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       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    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




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

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


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Dict):


						"""simple docstring"""




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

						# Test not batched input
						__UpperCAmelCase						:			Optional[int]												=       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
						__UpperCAmelCase						:			Optional[Any]												=       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    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__UpperCAmelCase						:			Tuple												=       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
						__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						:			List[str]												=       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    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__UpperCAmelCase						:			List[str]												=       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
						__UpperCAmelCase						:			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
						__UpperCAmelCase						:			Union[str, Any]												=       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    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       LayoutLMvaImageProcessor()

						from datasets import load_dataset

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

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

						__UpperCAmelCase						:			List[str]												=       image_processing(UpperCamelCase_   ,	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   ,	UpperCamelCase_)
						self.assertListEqual(encoding.boxes   ,	UpperCamelCase_)

						# with apply_OCR = False
						__UpperCAmelCase						:			Dict												=       LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase_)

						__UpperCAmelCase						:			Union[str, Any]												=       image_processing(UpperCamelCase_   ,	return_tensors="pt")

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










import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           # For consistency across different places the DisjunctiveConstraint is called,
           # dc.token_ids is a list of integers. It is also initialized only by integers.

           __lowerCAmelCase             =       [[1, 2, 4], [1, 2, 3, 4]]
           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)
           self.assertTrue(isinstance(dc.token_ids       ,    __UpperCamelCase						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           # We can't have constraints that are complete subsets of another. This leads to a preverse
           # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
           # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
           # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
           # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
           __lowerCAmelCase             =       [[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(__UpperCamelCase						)  # fails here


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(3						)
           __lowerCAmelCase             =       stepped is True and completed is True and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(4						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2, 4]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

           dc.reset()

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 3						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 2						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.remaining() == 0						)
           self.assertTrue(dc.current_seq == [1, 2, 5]						)






