






import os
from datetime import datetime as dt

from github import Github


__a       :	str             = [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]





def        SCREAMING_SNAKE_CASE       (      ):
					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 lowerCamelCase_: i.created_at ,     reverse=lowerCamelCase_)
										a__          =  comments[0] if len(lowerCamelCase_) > 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()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a       :	Tuple             = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = ['DeiTFeatureExtractor']
	__a       :	Optional[Any]             = ['DeiTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[int]             = [
	    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DeiTForImageClassification',
	    'DeiTForImageClassificationWithTeacher',
	    'DeiTForMaskedImageModeling',
	    'DeiTModel',
	    'DeiTPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Dict             = [
	    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDeiTForImageClassification',
	    'TFDeiTForImageClassificationWithTeacher',
	    'TFDeiTForMaskedImageModeling',
	    'TFDeiTModel',
	    'TFDeiTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

	__a       :	List[Any]             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


