






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


A_						:						str						      =					logging.get_logger(__name__)

A_						:						Dict						      =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_						:						Optional[int]						      =					{
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

A_						:						Union[str, Any]						      =					{
    'google/realm-cc-news-pretrained-embedder': 512,
    'google/realm-cc-news-pretrained-encoder': 512,
    'google/realm-cc-news-pretrained-scorer': 512,
    'google/realm-cc-news-pretrained-openqa': 512,
    'google/realm-orqa-nq-openqa': 512,
    'google/realm-orqa-nq-reader': 512,
    'google/realm-orqa-wq-openqa': 512,
    'google/realm-orqa-wq-reader': 512,
}

A_						:						Tuple						      =					{
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						str										=VOCAB_FILES_NAMES
       a							:						Any										=PRETRAINED_VOCAB_FILES_MAP
       a							:						List[Any]										=PRETRAINED_INIT_CONFIGURATION
       a							:						Optional[Any]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a							:						List[str]										=RealmTokenizer





       def __init__(   self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase="[UNK]"    ,     _lowerCamelCase="[SEP]"    ,     _lowerCamelCase="[PAD]"    ,     _lowerCamelCase="[CLS]"    ,     _lowerCamelCase="[MASK]"    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     **_lowerCamelCase    ,     ):
         super().__init__(
             _lowerCamelCase    ,     tokenizer_file=_lowerCamelCase    ,     do_lower_case=_lowerCamelCase    ,     unk_token=_lowerCamelCase    ,     sep_token=_lowerCamelCase    ,     pad_token=_lowerCamelCase    ,     cls_token=_lowerCamelCase    ,     mask_token=_lowerCamelCase    ,     tokenize_chinese_chars=_lowerCamelCase    ,     strip_accents=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

         UpperCamelCase_:   List[Any]        =							json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
         if (
             normalizer_state.get('lowercase'    ,     _lowerCamelCase				) != do_lower_case
             or normalizer_state.get('strip_accents'    ,     _lowerCamelCase				) != strip_accents
             or normalizer_state.get('handle_chinese_chars'    ,     _lowerCamelCase				) != tokenize_chinese_chars
         ):
           UpperCamelCase_:   Tuple        =							getattr(_lowerCamelCase    ,     normalizer_state.pop('type'				)				)
           UpperCamelCase_:   Any        =							do_lower_case
           UpperCamelCase_:   Tuple        =							strip_accents
           UpperCamelCase_:   Optional[Any]        =							tokenize_chinese_chars
           UpperCamelCase_:   List[str]        =							normalizer_class(**_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							do_lower_case





       def      _a       (   self    ,     _lowerCamelCase    ,     **_lowerCamelCase				):
         UpperCamelCase_:   Optional[Any]        =							PaddingStrategy.MAX_LENGTH

         UpperCamelCase_:   Any        =							text
         UpperCamelCase_:   List[Any]        =							kwargs.pop('text_pair'    ,     _lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							kwargs.pop('return_tensors'    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							{
             'input_ids': [],
             'attention_mask': [],
             'token_type_ids': [],
         }

         for idx, candidate_text in enumerate(_lowerCamelCase				):
           if batch_text_pair is not None:
             UpperCamelCase_:   Union[str, Any]        =							batch_text_pair[idx]
           else:
             UpperCamelCase_:   Any        =							None

           UpperCamelCase_:   Any        =							super().__call__(_lowerCamelCase    ,     _lowerCamelCase    ,     return_tensors=_lowerCamelCase    ,     **_lowerCamelCase				)

           UpperCamelCase_:   Dict        =							encoded_candidates.get('input_ids'				)
           UpperCamelCase_:   str        =							encoded_candidates.get('attention_mask'				)
           UpperCamelCase_:   int        =							encoded_candidates.get('token_type_ids'				)

           if encoded_input_ids is not None:
             output_data["input_ids"].append(_lowerCamelCase				)
           if encoded_attention_mask is not None:
             output_data["attention_mask"].append(_lowerCamelCase				)
           if encoded_token_type_ids is not None:
             output_data["token_type_ids"].append(_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							{key: item for key, item in output_data.items() if len(_lowerCamelCase				) != 0}

         return BatchEncoding(_lowerCamelCase    ,     tensor_type=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Union[str, Any]        =							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

         return output





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   Union[str, Any]        =							[self.sep_token_id]
         UpperCamelCase_:   Dict        =							[self.cls_token_id]
         if token_ids_a is None:
           return len(cls + token_ids_a + sep				) * [0]
         return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase = None				):
         UpperCamelCase_:   Tuple        =							self._tokenizer.model.save(_lowerCamelCase    ,     name=_lowerCamelCase				)
         return tuple(_lowerCamelCase				)

"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


A__ :       Tuple				= [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  Github(os.environ['''GITHUB_TOKEN''']    )
  _lowercase:      Optional[int]   			=  g.get_repo('''huggingface/diffusers'''    )
  _lowercase:      Union[str, Any]   			=  repo.get_issues(state='''open'''    )

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[Any]   			=  comments[0] if len(_UpperCamelCase    ) > 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()    )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''    )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''    )
      issue.remove_from_labels('''stale'''    )
    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()    )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      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/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''    )
      issue.add_to_labels('''stale'''    )


if __name__ == "__main__":
  main()
