






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


snake_case     					=		logging.get_logger(__name__)


snake_case     					=		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case     					=		{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

snake_case     					=		{
    """allenai/led-base-16384""": 16_384,
}


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[int]								=						VOCAB_FILES_NAMES
     UpperCamelCase_				:		Optional[Any]								=						PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_				:		Union[str, Any]								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_				:		Optional[Any]								=						LEDTokenizer
     UpperCamelCase_				:		Union[str, Any]								=						['''input_ids''', '''attention_mask''']


     def __init__(					self							:						List[str]       ,			UpperCAmelCase_							:						Tuple=None       ,			UpperCAmelCase_							:						Dict=None       ,			UpperCAmelCase_							:						Optional[int]=None       ,			UpperCAmelCase_							:						Dict="replace"       ,			UpperCAmelCase_							:						Any="<s>"       ,			UpperCAmelCase_							:						Optional[int]="</s>"       ,			UpperCAmelCase_							:						Optional[int]="</s>"       ,			UpperCAmelCase_							:						Union[str, Any]="<s>"       ,			UpperCAmelCase_							:						Tuple="<unk>"       ,			UpperCAmelCase_							:						Tuple="<pad>"       ,			UpperCAmelCase_							:						Optional[int]="<mask>"       ,			UpperCAmelCase_							:						Any=False       ,			UpperCAmelCase_							:						Dict=True       ,			**UpperCAmelCase_							:						Union[str, Any]       ,			):
       super().__init__(
           UpperCAmelCase_       ,			UpperCAmelCase_       ,			tokenizer_file=UpperCAmelCase_       ,			errors=UpperCAmelCase_       ,			bos_token=UpperCAmelCase_       ,			eos_token=UpperCAmelCase_       ,			sep_token=UpperCAmelCase_       ,			cls_token=UpperCAmelCase_       ,			unk_token=UpperCAmelCase_       ,			pad_token=UpperCAmelCase_       ,			mask_token=UpperCAmelCase_       ,			add_prefix_space=UpperCAmelCase_       ,			trim_offsets=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   Any						    =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
       if pre_tok_state.get("add_prefix_space"       ,			UpperCAmelCase_   ) != add_prefix_space:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     getattr(UpperCAmelCase_       ,			pre_tok_state.pop("type"   )   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     add_prefix_space
         SCREAMING_SNAKE_CASE							:   Dict						    =     pre_tok_class(**UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       SCREAMING_SNAKE_CASE							:   int						    =     "post_processor"
       SCREAMING_SNAKE_CASE							:   List[str]						    =     getattr(self.backend_tokenizer       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
       if tokenizer_component_instance:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     json.loads(tokenizer_component_instance.__getstate__()   )

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tuple(state["sep"]   )
         if "cls" in state:
           SCREAMING_SNAKE_CASE							:   str						    =     tuple(state["cls"]   )

         SCREAMING_SNAKE_CASE							:   List[str]						    =     False

         if state.get("add_prefix_space"       ,			UpperCAmelCase_   ) != add_prefix_space:
           SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     add_prefix_space
           SCREAMING_SNAKE_CASE							:   Any						    =     True

         if state.get("trim_offsets"       ,			UpperCAmelCase_   ) != trim_offsets:
           SCREAMING_SNAKE_CASE							:   Any						    =     trim_offsets
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     True

         if changes_to_apply:
           SCREAMING_SNAKE_CASE							:   List[str]						    =     getattr(UpperCAmelCase_       ,			state.pop("type"   )   )
           SCREAMING_SNAKE_CASE							:   Any						    =     component_class(**UpperCAmelCase_   )
           setattr(self.backend_tokenizer       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )


     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def 					_A							(					self							:						Tuple   ):
       if self._mask_token is None:
         if self.verbose:
           logger.error("Using mask_token, but it is not set yet."   )
         return None
       return str(self._mask_token   )


     @mask_token.setter
     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     AddedToken(UpperCAmelCase_       ,			lstrip=UpperCAmelCase_       ,			rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ) else value
       SCREAMING_SNAKE_CASE							:   Tuple						    =     value


     def 					_A							(					self							:						Optional[Any]       ,			*UpperCAmelCase_							:						int       ,			**UpperCAmelCase_							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     kwargs.get("is_split_into_words"       ,			UpperCAmelCase_   )

       if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."   )

       return super()._batch_encode_plus(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[Any]       ,			*UpperCAmelCase_							:						Any       ,			**UpperCAmelCase_							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     kwargs.get("is_split_into_words"       ,			UpperCAmelCase_   )

       if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."   )

       return super()._encode_plus(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str] = None   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self._tokenizer.model.save(UpperCAmelCase_       ,			name=UpperCAmelCase_   )
       return tuple(UpperCAmelCase_   )


     def 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Union[str, Any]=None   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
         return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     [self.sep_token_id]
       SCREAMING_SNAKE_CASE							:   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 + sep + token_ids_a + sep   ) * [0]






     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Union[Dict[str, EncodedInput], BatchEncoding]       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						Optional[bool] = None       ,			):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     super()._pad(
           encoded_inputs=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			padding_strategy=UpperCAmelCase_       ,			pad_to_multiple_of=UpperCAmelCase_       ,			return_attention_mask=UpperCAmelCase_       ,			)

       # Load from model defaults
       if return_attention_mask is None:
         SCREAMING_SNAKE_CASE							:   int						    =     "attention_mask" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
         SCREAMING_SNAKE_CASE							:   int						    =     encoded_inputs[self.model_input_names[0]]
         # `global_attention_mask` need to have the same length as other (sequential) inputs.
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     len(encoded_inputs["global_attention_mask"]   ) != len(UpperCAmelCase_   )

         if needs_to_be_padded:
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     len(UpperCAmelCase_   ) - len(encoded_inputs["global_attention_mask"]   )

           if self.padding_side == "right":
             # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
             SCREAMING_SNAKE_CASE							:   Any						    =     (
                 encoded_inputs["global_attention_mask"] + [-1] * difference
             )
           elif self.padding_side == "left":
             SCREAMING_SNAKE_CASE							:   str						    =     [-1] * difference + encoded_inputs[
                 "global_attention_mask"
             ]
           else:
             raise ValueError("Invalid padding strategy:" + str(self.padding_side   )   )

       return encoded_inputs













'''simple docstring'''






from datetime import datetime as dt
import os

from github import Github


A__							:  str    =[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				Github(os.environ["""GITHUB_TOKEN"""]    )
      _lowerCAmelCase					=				g.get_repo("""huggingface/transformers"""    )
      _lowerCAmelCase					=				repo.get_issues(state="""open"""    )

      for issue in open_issues:
            _lowerCAmelCase					=				sorted([comment for comment in issue.get_comments()]				,      key=lambda lowerCAmelCase    : i.created_at				,      reverse=lowerCAmelCase    )
            _lowerCAmelCase					=				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()






