
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


lowerCAmelCase					:    Dict           = logging.get_logger(__name__)

lowerCAmelCase					:    str           = '▁'

lowerCAmelCase					:    List[str]           = {'vocab_file': 'prophetnet.tokenizer'}

lowerCAmelCase					:    Any           = {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

lowerCAmelCase					:    Union[str, Any]           = {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

lowerCAmelCase					:    Dict           = {
    'microsoft/xprophetnet-large-wiki100-cased': 5_12,
}







def 		A_(     A						:		Optional[Any]):
							UpperCamelCase   			=     collections.OrderedDict()
							with open(A		,     'r'		,     encoding='utf-8') as reader:
														UpperCamelCase   			=     reader.readlines()
							for index, token in enumerate(A):
														UpperCamelCase   			=     token.rstrip('\n')
														UpperCamelCase   			=     index
							return vocab





class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							VOCAB_FILES_NAMES
				lowerCAmelCase_     			=							PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase_     			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase_     			=							["""input_ids""", """attention_mask"""]







				def __init__(       self						,				A_						,				A_="[SEP]"						,				A_="[SEP]"						,				A_="[SEP]"						,				A_="[UNK]"						,				A_="[PAD]"						,				A_="[CLS]"						,				A_="[MASK]"						,				A_ = None						,				**A_						,				)->		None:







											'''simple docstring'''






											UpperCamelCase   			=     {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=A_						,				eos_token=A_						,				sep_token=A_						,				unk_token=A_						,				pad_token=A_						,				cls_token=A_						,				mask_token=A_						,				sp_model_kwargs=self.sp_model_kwargs						,				**A_						,				)

											try:
																		import sentencepiece as spm
											except ImportError:
																		logger.warning(
																		    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
																		    ' pip install sentencepiece'     )
																		raise

											UpperCamelCase   			=     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
											self.sp_model.Load(str(A_     )     )
											UpperCamelCase   			=     vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# put special tokens and [unused] tokens into the vocab
											UpperCamelCase   			=     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

											for i in range(10     ):
																		UpperCamelCase   			=     F'''[unused{i}]'''
																		UpperCamelCase   			=     5 + i

											# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
											UpperCamelCase   			=     12
											UpperCamelCase   			=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
											for k in self.fairseq_tokens_to_ids.keys():
																		self.unique_no_split_tokens.append(A_     )







				def __getstate__(       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     self.__dict__.copy()
											UpperCamelCase   			=     None
											return state







				def __setstate__(       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     d
											try:
																		import sentencepiece as spm
											except ImportError:
																		logger.warning(
																		    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
																		    ' pip install sentencepiece'     )
																		raise

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

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = False     )->		List[int]:







											'''simple docstring'''






											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=A_						,				token_ids_a=A_						,				already_has_special_tokens=A_     )

											if token_ids_a is None:
																		return ([0] * len(A_     )) + [1]
											return ([0] * len(A_     )) + [1] + ([0] * len(A_     )) + [1]







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											UpperCamelCase   			=     [self.sep_token_id]

											if token_ids_a is None:
																		return len(token_ids_a + sep     ) * [0]
											return len(token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											return len(self.sp_model     ) + self.fairseq_offset







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {self.convert_ids_to_tokens(A_     ): i for i in range(self.vocab_size     )}
											vocab.update(self.added_tokens_encoder     )
											return vocab







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											return self.sp_model.encode(A_						,				out_type=A_     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		Union[str, Any]:







											'''simple docstring'''






											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											UpperCamelCase   			=     self.sp_model.PieceToId(A_     )

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											if index in self.fairseq_ids_to_tokens:
																		return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     ''.join(A_     ).replace(A_						,				' '     ).strip()
											return out_string







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		Tuple[str]:







											'''simple docstring'''






											if not os.path.isdir(A_     ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
																		return
											UpperCamelCase   			=     os.path.join(
											    A_						,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

											return (out_vocab_file,)







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[int]:







											'''simple docstring'''






											if token_ids_a is None:
																		return token_ids_a + [self.sep_token_id]
											UpperCamelCase   			=     [self.sep_token_id]
											return token_ids_a + sep + token_ids_a + sep






import os
from datetime import datetime as dt

from github import Github


__magic_name__  							=	[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Github(os.environ["GITHUB_TOKEN"]				)
    snake_case__    =					g.get_repo("huggingface/accelerate"				)
    snake_case__    =					repo.get_issues(state="open"				)

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



