




from datetime import datetime as dt
import os

from github import Github


lowerCAmelCase_		      = [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]






def 					snake_case(				) ->							List[Any]:





     '''simple docstring'''


     lowercase					:   Any								= Github(os.environ['''GITHUB_TOKEN''']    )
     lowercase					:   Optional[int]								= g.get_repo('''huggingface/transformers'''    )
     lowercase					:   Dict								= repo.get_issues(state='''open'''    )

     for issue in open_issues:
          lowercase					:   Optional[int]								= sorted([comment for comment in issue.get_comments()] ,		key=lambda __magic_name__    : i.created_at ,		reverse=__lowerCamelCase    )
          lowercase					:   int								= comments[0] if len(__lowerCamelCase    ) > 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()







import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	'MCTCTFeatureExtractor'
      a  : str							       =	'AutoTokenizer'
      def __init__(      self					:			Tuple		,      __lowercase					:			int		,      __lowercase					:			Dict					)							->      Any:
        super().__init__(__lowercase		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	Optional[int]           =  False
      def __call__(      self					:			int		,      *__lowercase					:			Tuple		,      **__lowercase					:			Optional[int]					)							->      Union[str, Any]:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*__lowercase		,      **__lowercase					)

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""					)
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""raw_speech"""					)
        else:
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""audio"""		,      __lowercase					)
        __UpperCAmelCase							:	List[str]           =  kwargs.pop("""sampling_rate"""		,      __lowercase					)
        __UpperCAmelCase							:	Tuple           =  kwargs.pop("""text"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Tuple           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""					)

        if audio is not None:
          __UpperCAmelCase							:	Tuple           =  self.feature_extractor(__lowercase		,      *__lowercase		,      sampling_rate=__lowercase		,      **__lowercase					)
        if text is not None:
          __UpperCAmelCase							:	str           =  self.tokenizer(__lowercase		,      **__lowercase					)

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          __UpperCAmelCase							:	Dict           =  encodings["""input_ids"""]
          return inputs
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      *__lowercase					:			List[Any]		,      **__lowercase					:			int					)							->      List[Any]:
        return self.tokenizer.batch_decode(*__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      *__lowercase					:			Optional[Any]		,      **__lowercase					:			List[str]					)							->      int:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor.pad(*__lowercase		,      **__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  kwargs.pop("""input_features"""		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  kwargs.pop("""labels"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Union[str, Any]           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if input_features is not None:
          __UpperCAmelCase							:	Any           =  self.feature_extractor.pad(__lowercase		,      *__lowercase		,      **__lowercase					)
        if labels is not None:
          __UpperCAmelCase							:	Union[str, Any]           =  self.tokenizer.pad(__lowercase		,      **__lowercase					)

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          __UpperCAmelCase							:	Any           =  labels["""input_ids"""]
          return input_features
      def 	UpperCAmelCase      (      self					:			Any		,      *__lowercase					:			Union[str, Any]		,      **__lowercase					:			Dict					)							->      List[Any]:
        return self.tokenizer.decode(*__lowercase		,      **__lowercase					)




      @contextmanager
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""					)
        __UpperCAmelCase							:	Any           =  True
        __UpperCAmelCase							:	Optional[int]           =  self.tokenizer
        yield
        __UpperCAmelCase							:	List[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	int           =  False


