



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

from github import Github


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





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			=Github(os.environ["""GITHUB_TOKEN"""] )
   SCREAMING_SNAKE_CASE_: List[str]     			=g.get_repo("""huggingface/accelerate""" )
   SCREAMING_SNAKE_CASE_: Tuple     			=repo.get_issues(state="""open""" )

   for issue in open_issues:
      SCREAMING_SNAKE_CASE_: int     			=sorted([comment for comment in issue.get_comments()]							,			key=lambda lowercase : i.created_at							,			reverse=lowercase )
      SCREAMING_SNAKE_CASE_: int     			=comments[0] if len(lowercase ) > 0 else None
      SCREAMING_SNAKE_CASE_: Optional[int]     			=dt.utcnow()
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=(current_time - issue.updated_at).days
      SCREAMING_SNAKE_CASE_: Tuple     			=(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()








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=list(readme_content.splitlines() )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=full_content[1:].index("""---""" ) + 1
      SCREAMING_SNAKE_CASE_: List[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




     if readme_content is not None:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=_split_yaml_from_readme(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[str]     			="""---\n""" + self.to_yaml_string() + """---\n""" + content
     else:
        SCREAMING_SNAKE_CASE_: List[Any]     			="""---\n""" + self.to_yaml_string() + """---\n"""
     return full_content



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

     # Convert the YAML keys to DatasetMetadata fields
     SCREAMING_SNAKE_CASE_: List[Any]     			={
         (key.replace("""-"""       ,		"""_"""		) if key.replace("""-"""       ,		"""_"""		) in cls._FIELDS_WITH_DASHES else key): value
         for key, value in metadata_dict.items()
     }
     return cls(**lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     return yaml.safe_dump(
         {
             (key.replace("""_"""       ,		"""-"""		) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }       ,		sort_keys=lowerCAmelCase       ,		allow_unicode=lowerCAmelCase       ,		encoding="""utf-8"""       ,		).decode("""utf-8"""		)







_UpperCAmelCase     =			{
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _UpperCAmelCase     =			ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
   ap.add_argument("""readme_filepath""")
   _UpperCAmelCase     =			ap.parse_args()

   _UpperCAmelCase     =			Path(args.readme_filepath)
   _UpperCAmelCase     =			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)



