




from datetime import datetime as dt
import os

from github import Github


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

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    Github(os.environ['''GITHUB_TOKEN''']     )
							SCREAMING_SNAKE_CASE_   	=    g.get_repo('''huggingface/transformers'''     )
							SCREAMING_SNAKE_CASE_   	=    repo.get_issues(state='''open'''     )

							for issue in open_issues:
														SCREAMING_SNAKE_CASE_   	=    sorted([comment for comment in issue.get_comments()]  ,    key=lambda _lowerCAmelCase     : i.created_at  ,    reverse=_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    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 >= 3_0
														    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 > 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()     )
														):
																					# 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE 						="""▁"""

__SCREAMING_SNAKE_CASE 						={"""vocab_file""": """spiece.model"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

__SCREAMING_SNAKE_CASE 						={
    """google/reformer-crime-and-punishment""": 524_288,
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["input_ids", "attention_mask"]


				def __init__(							self:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[Any]="</s>"		,					_lowerCamelCase:   str="<unk>"		,					_lowerCamelCase:   Union[str, Any]=[]		,					_lowerCamelCase:   Optional[Dict[str, Any]] = None		,					**_lowerCamelCase:   List[str]		,					):
											SCREAMING_SNAKE_CASE_   	=    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=_lowerCamelCase		,					unk_token=_lowerCamelCase		,					additional_special_tokens=_lowerCamelCase		,					sp_model_kwargs=self.sp_model_kwargs		,					**_lowerCamelCase		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(_lowerCamelCase		)


				@property
				def     _A							(							self:   Optional[int]		):
											return self.sp_model.get_piece_size()


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    {self.convert_ids_to_tokens(_lowerCamelCase		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


				def __getstate__(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.__dict__.copy()
											SCREAMING_SNAKE_CASE_   	=    None
											return state


				def __setstate__(							self:   Dict		,					_lowerCamelCase:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    d

											# for backward compatibility
											if not hasattr(self		,					'''sp_model_kwargs'''		):
																		SCREAMING_SNAKE_CASE_   	=    {}

											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   str		):
											return self.sp_model.encode(_lowerCamelCase		,					out_type=_lowerCamelCase		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[int]		):
											return self.sp_model.piece_to_id(_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Tuple		):
											if index < self.sp_model.get_piece_size():
																		SCREAMING_SNAKE_CASE_   	=    self.sp_model.IdToPiece(_lowerCamelCase		)
											return token


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    []
											SCREAMING_SNAKE_CASE_   	=    ''''''
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(_lowerCamelCase		) + token
																									SCREAMING_SNAKE_CASE_   	=    []
																		else:
																									current_sub_tokens.append(_lowerCamelCase		)
											out_string += self.sp_model.decode(_lowerCamelCase		)
											return out_string.strip()





				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    _lowerCamelCase		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		) and os.path.isfile(self.vocab_file		):
																		copyfile(self.vocab_file		,					_lowerCamelCase		)
											elif not os.path.isfile(self.vocab_file		):
																		with open(_lowerCamelCase		,					'''wb'''		) as fi:
																									SCREAMING_SNAKE_CASE_   	=    self.sp_model.serialized_model_proto()
																									fi.write(_lowerCamelCase		)

											return (out_vocab_file,)


