



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__  :  Tuple								=					{
    '''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Any								=					['''AlbertTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Any								=					['''AlbertTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Union[str, Any]								=					[
						    '''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''AlbertForMaskedLM''',
						    '''AlbertForMultipleChoice''',
						    '''AlbertForPreTraining''',
						    '''AlbertForQuestionAnswering''',
						    '''AlbertForSequenceClassification''',
						    '''AlbertForTokenClassification''',
						    '''AlbertModel''',
						    '''AlbertPreTrainedModel''',
						    '''load_tf_weights_in_albert''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  List[str]								=					[
						    '''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFAlbertForMaskedLM''',
						    '''TFAlbertForMultipleChoice''',
						    '''TFAlbertForPreTraining''',
						    '''TFAlbertForQuestionAnswering''',
						    '''TFAlbertForSequenceClassification''',
						    '''TFAlbertForTokenClassification''',
						    '''TFAlbertMainLayer''',
						    '''TFAlbertModel''',
						    '''TFAlbertPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Optional[int]								=					[
						    '''FlaxAlbertForMaskedLM''',
						    '''FlaxAlbertForMultipleChoice''',
						    '''FlaxAlbertForPreTraining''',
						    '''FlaxAlbertForQuestionAnswering''',
						    '''FlaxAlbertForSequenceClassification''',
						    '''FlaxAlbertForTokenClassification''',
						    '''FlaxAlbertModel''',
						    '''FlaxAlbertPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_albert import AlbertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_albert_fast import AlbertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_albert import (
												    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AlbertForMaskedLM,
												    AlbertForMultipleChoice,
												    AlbertForPreTraining,
												    AlbertForQuestionAnswering,
												    AlbertForSequenceClassification,
												    AlbertForTokenClassification,
												    AlbertModel,
												    AlbertPreTrainedModel,
												    load_tf_weights_in_albert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_albert import (
												    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFAlbertForMaskedLM,
												    TFAlbertForMultipleChoice,
												    TFAlbertForPreTraining,
												    TFAlbertForQuestionAnswering,
												    TFAlbertForSequenceClassification,
												    TFAlbertForTokenClassification,
												    TFAlbertMainLayer,
												    TFAlbertModel,
												    TFAlbertPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_albert import (
												    FlaxAlbertForMaskedLM,
												    FlaxAlbertForMultipleChoice,
												    FlaxAlbertForPreTraining,
												    FlaxAlbertForQuestionAnswering,
												    FlaxAlbertForSequenceClassification,
												    FlaxAlbertForTokenClassification,
												    FlaxAlbertModel,
												    FlaxAlbertPreTrainedModel,
												)
else:
						import sys

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










from datetime import datetime as dt
import os

from github import Github


A__  :  List[str]								=					[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Union[str, Any]									= Github(os.environ['''GITHUB_TOKEN'''] )
		lowerCAmelCase_			:     Tuple									= g.get_repo('''huggingface/transformers''' )
		lowerCAmelCase_			:     int									= repo.get_issues(state='''open''' )

		for issue in open_issues:
				lowerCAmelCase_			:     Optional[Any]									= sorted([comment for comment in issue.get_comments()]     ,key=lambda __UpperCamelCase : i.created_at     ,reverse=__UpperCamelCase )
				lowerCAmelCase_			:     Tuple									= 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()





