'''simple docstring'''






import os
from datetime import datetime as dt

from github import Github


_UpperCAmelCase    :     List[Any]         =       [
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





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

     for issue in open_issues:
          _A    		=  sorted([comment for comment in issue.get_comments()]   ,		key=lambda __snake_case				: i.created_at   ,		reverse=__snake_case				)
          _A    		=  comments[0] if len(__snake_case				) > 0 else None
          _A    		=  dt.utcnow()
          _A    		=  (current_time - issue.updated_at).days
          _A    		=  (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 >= 3_0
              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 > 2_3
              and days_since_creation >= 3_0
              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()




'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     List[Any]         =       {
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2TokenizerFast''']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       ['''LayoutLMv2FeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''LayoutLMv2ImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     int         =       [
       '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LayoutLMv2ForQuestionAnswering''',
       '''LayoutLMv2ForSequenceClassification''',
       '''LayoutLMv2ForTokenClassification''',
       '''LayoutLMv2Layer''',
       '''LayoutLMv2Model''',
       '''LayoutLMv2PreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

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



