


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor



class 			lowerCamelCase__						(   unittest.TestCase			):







						def __init__(							self   :    Any			,			_lowercase   :    List[Any]			,			_lowercase   :    int=7			,			_lowercase   :    List[str]=3			,			_lowercase   :    Tuple=18			,			_lowercase   :    Optional[int]=30			,			_lowercase   :    Union[str, Any]=400			,			_lowercase   :    int=True			,			_lowercase   :    Tuple=32			,			_lowercase   :    List[str]=True			,			):
								A					       = parent
								A					       = batch_size
								A					       = num_channels
								A					       = image_size
								A					       = min_resolution
								A					       = max_resolution
								A					       = do_resize
								A					       = size_divisor
								A					       = do_rescale







						def __a						(							self   :    List[str]		):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}



@require_torch
@require_vision
class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     GLPNImageProcessor if is_vision_available() else None







						def __a						(							self   :    Tuple		):
								A					       = GLPNImageProcessingTester(self		)







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







						def __a						(							self   :    Optional[int]		):
								A					       = self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(_lowercase			,			'do_resize'		)		)
								self.assertTrue(hasattr(_lowercase			,			'size_divisor'		)		)
								self.assertTrue(hasattr(_lowercase			,			'resample'		)		)
								self.assertTrue(hasattr(_lowercase			,			'do_rescale'		)		)







						def __a						(							self   :    Tuple		):
								pass







						def __a						(							self   :    int		):
								# 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=_lowercase		)
								for image in image_inputs:
										self.assertIsInstance(_lowercase			,			Image.Image		)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								A					       = image_processing(image_inputs[0]			,			return_tensors='pt'		).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)







						def __a						(							self   :    List[Any]		):
								# 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=_lowercase			,			numpify=_lowercase		)
								for image in image_inputs:
										self.assertIsInstance(_lowercase			,			np.ndarray		)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								A					       = image_processing(image_inputs[0]			,			return_tensors='pt'		).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)







						def __a						(							self   :    Any		):
								# 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=_lowercase			,			torchify=_lowercase		)
								for image in image_inputs:
										self.assertIsInstance(_lowercase			,			torch.Tensor		)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								A					       = image_processing(image_inputs[0]			,			return_tensors='pt'		).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)









"""simple docstring"""


from datetime import datetime as dt
import os

from github import Github


UpperCamelCase						:				Union[str, Any]             =      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		A					       = Github(os.environ['GITHUB_TOKEN']	)
		A					       = g.get_repo('huggingface/transformers'	)
		A					       = repo.get_issues(state='open'	)

		for issue in open_issues:
				A					       = sorted([comment for comment in issue.get_comments()] ,			key=lambda UpperCamelCase__	: i.created_at ,			reverse=UpperCamelCase__	)
				A					       = comments[0] if len(UpperCamelCase__	) > 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()	)
				):
						# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
						issue.edit(state='closed'	)
				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()	)
				):
						# print(f"Would add stale comment to {issue.number}")
						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/transformers/blob/main/CONTRIBUTING.md) '
						    'are likely to be ignored.'	)


if __name__ == "__main__":
	main()





