"""simple docstring"""
import os
from datetime import datetime as dt

from github import Github


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





def 				lowerCAmelCase__      (     )   ->			Dict:





						"""simple docstring"""






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

						for issue in open_issues:
												snake_case        =     sorted(issue.get_comments()	,     key=lambda _UpperCamelCase   : i.created_at	,     reverse=_UpperCamelCase   )
												snake_case        =     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 >= 3_0
												    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 > 2_3
												    and (dt.utcnow() - issue.created_at).days >= 3_0
												    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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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


