



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  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=7  ,  a_=3  ,  a_=18  ,  a_=30  ,  a_=400  ,  a_=True  ,  a_=None  ,  a_=True  ,  ):
											a__				      =		size if size is not None else {"""height""": 18, """width""": 18}
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		num_channels
											a__				      =		image_size
											a__				      =		min_resolution
											a__				      =		max_resolution
											a__				      =		do_resize
											a__				      =		size
											a__				      =		apply_ocr







							def _a      (							self		):
											return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					LayoutLMvaImageProcessor if is_pytesseract_available() else None


							def _a      (							self		):
											a__				      =		LayoutLMvaImageProcessingTester(self		)


							@property
							def _a      (							self		):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a      (							self		):
											a__				      =		self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(a_  ,  """do_resize"""		)		)
											self.assertTrue(hasattr(a_  ,  """size"""		)		)
											self.assertTrue(hasattr(a_  ,  """apply_ocr"""		)		)


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

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


							def _a      (							self		):
											pass


							def _a      (							self		):
											# Initialize image_processing
											a__				      =		self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											a__				      =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_  ,  Image.Image		)

											# Test not batched input
											a__				      =		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  ,  a_		)
											self.assertIsInstance(encoding.boxes  ,  a_		)

											# Test batched
											a__				      =		image_processing(a_  ,  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		):
											# Initialize image_processing
											a__				      =		self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											a__				      =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=a_  ,  numpify=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_  ,  np.ndarray		)

											# Test not batched input
											a__				      =		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__				      =		image_processing(a_  ,  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		):
											# Initialize image_processing
											a__				      =		self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											a__				      =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=a_  ,  torchify=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_  ,  torch.Tensor		)

											# Test not batched input
											a__				      =		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__				      =		image_processing(a_  ,  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		):
											# with apply_OCR = True
											a__				      =		LayoutLMvaImageProcessor()

											from datasets import load_dataset

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

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

											a__				      =		image_processing(a_  ,  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__				      =		[["""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__				      =		[[[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  ,  a_		)
											self.assertListEqual(encoding.boxes  ,  a_		)

											# with apply_OCR = False
											a__				      =		LayoutLMvaImageProcessor(apply_ocr=a_		)

											a__				      =		image_processing(a_  ,  return_tensors="""pt"""		)

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






import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase							 =					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]

def 			A_      (	):

				"""simple docstring"""
				a__				      =		Github(os.environ["""GITHUB_TOKEN"""]		)
				a__				      =		g.get_repo("""huggingface/diffusers"""		)
				a__				      =		repo.get_issues(state="""open"""		)

				for issue in open_issues:
								a__				      =		sorted(issue.get_comments() ,		key=lambda __a		: i.created_at ,		reverse=__a		)
								a__				      =		comments[0] if len(__a		) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
								):
												# Closes the issue after 7 days of inactivity since the Stalebot notification.
												issue.edit(state="""closed"""		)
								elif (
								    "stale" in issue.get_labels()
								    and last_comment is not None
								    and last_comment.user.login != "github-actions[bot]"
								):
												# Opens the issue if someone other than Stalebot commented.
												issue.edit(state="""open"""		)
												issue.remove_from_labels("""stale"""		)
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
								):
												# Post a Stalebot notification after 23 days of inactivity.
												issue.create_comment(
												    """This issue has been automatically marked as stale because it has not had """
												    """recent activity. If you think this still needs to be addressed """
												    """please comment on this thread.\n\nPlease note that issues that do not follow the """
												    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
												    """are likely to be ignored."""		)
												issue.add_to_labels("""stale"""		)


if __name__ == "__main__":
	main()

