





'''simple docstring'''





import os
from datetime import datetime as dt

from github import Github


_a						=   [
    "good first issue",
    "feature request",
    "wip",
]





def lowerCAmelCase__()       ->   str:







	'''simple docstring'''







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

	for issue in open_issues:
		lowerCamelCase__              =	sorted([comment for comment in issue.get_comments()] ,key=lambda __snake_case							: i.created_at ,reverse=__snake_case							)
		lowerCamelCase__              =	comments[0] if len(__snake_case							) > 0 else None
		lowerCamelCase__              =	dt.utcnow()
		lowerCamelCase__              =	(current_time - issue.updated_at).days
		lowerCamelCase__              =	(current_time - issue.created_at).days
		if (
		    last_comment is not None
		    and last_comment.user.login == "github-actions[bot]"
		    and days_since_updated > 7
		    and days_since_creation >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Close issue since it has been 7 days of inactivity since bot mention.
			issue.edit(state='''closed'''							)
		elif (
		    days_since_updated > 23
		    and days_since_creation >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Add stale comment
			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/accelerate/blob/main/CONTRIBUTING.md) '''
			    '''are likely to be ignored.'''							)


if __name__ == "__main__":
			main()










from typing import TYPE_CHECKING

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


_a						=   {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   ["FunnelTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FunnelBaseModel",
			    "FunnelForMaskedLM",
			    "FunnelForMultipleChoice",
			    "FunnelForPreTraining",
			    "FunnelForQuestionAnswering",
			    "FunnelForSequenceClassification",
			    "FunnelForTokenClassification",
			    "FunnelModel",
			    "FunnelPreTrainedModel",
			    "load_tf_weights_in_funnel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFFunnelBaseModel",
			    "TFFunnelForMaskedLM",
			    "TFFunnelForMultipleChoice",
			    "TFFunnelForPreTraining",
			    "TFFunnelForQuestionAnswering",
			    "TFFunnelForSequenceClassification",
			    "TFFunnelForTokenClassification",
			    "TFFunnelModel",
			    "TFFunnelPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



