



'''simple docstring'''


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


_lowerCamelCase	:       Optional[int]      	=	logging.get_logger(__name__)


_lowerCamelCase	:       Union[str, Any]      	=	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase	:       List[str]      	=	{
    "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",
    },
}

_lowerCamelCase	:       Optional[Any]      	=	{
    "allenai/led-base-16384": 1_6384,
}

class  SCREAMING_SNAKE_CASE (  _SCREAMING_SNAKE_CASE		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  VOCAB_FILES_NAMES
    _SCREAMING_SNAKE_CASE	  =  PRETRAINED_VOCAB_FILES_MAP
    _SCREAMING_SNAKE_CASE	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _SCREAMING_SNAKE_CASE	  =  LEDTokenizer
    _SCREAMING_SNAKE_CASE	  =  ["""input_ids""", """attention_mask"""]






    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					List[Any]=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					List[str]="replace"  ,						UpperCamelCase__      :					int="<s>"  ,						UpperCamelCase__      :					Tuple="</s>"  ,						UpperCamelCase__      :					Optional[int]="</s>"  ,						UpperCamelCase__      :					Optional[int]="<s>"  ,						UpperCamelCase__      :					Dict="<unk>"  ,						UpperCamelCase__      :					List[str]="<pad>"  ,						UpperCamelCase__      :					Optional[Any]="<mask>"  ,						UpperCamelCase__      :					Optional[Any]=False  ,						UpperCamelCase__      :					Tuple=True  ,						**UpperCamelCase__      :					Any  ,						):


       """simple docstring"""







       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__  ,						)

       UpperCamelCase					=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get('add_prefix_space'  ,						UpperCamelCase__ ) != add_prefix_space:
          UpperCamelCase					=  getattr(UpperCamelCase__  ,						pre_tok_state.pop('type' ) )
          UpperCamelCase					=  add_prefix_space
          UpperCamelCase					=  pre_tok_class(**UpperCamelCase__ )

       UpperCamelCase					=  add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase					=  """post_processor"""
       UpperCamelCase					=  getattr(self.backend_tokenizer  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       if tokenizer_component_instance:
          UpperCamelCase					=  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:
             UpperCamelCase					=  tuple(state['sep'] )
          if "cls" in state:
             UpperCamelCase					=  tuple(state['cls'] )

          UpperCamelCase					=  False

          if state.get('add_prefix_space'  ,						UpperCamelCase__ ) != add_prefix_space:
             UpperCamelCase					=  add_prefix_space
             UpperCamelCase					=  True

          if state.get('trim_offsets'  ,						UpperCamelCase__ ) != trim_offsets:
             UpperCamelCase					=  trim_offsets
             UpperCamelCase					=  True

          if changes_to_apply:
             UpperCamelCase					=  getattr(UpperCamelCase__  ,						state.pop('type' ) )
             UpperCamelCase					=  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      :					Optional[int] ):


       """simple docstring"""







       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      :					Optional[Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  AddedToken(UpperCamelCase__  ,						lstrip=UpperCamelCase__  ,						rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__  ,						UpperCamelCase__ ) else value
       UpperCamelCase					=  value






    def  A       (  self      :					List[Any]  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  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      :					str  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  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      :					str  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Optional[str] = None ):


       """simple docstring"""







       UpperCamelCase					=  self._tokenizer.model.save(UpperCamelCase__  ,						name=UpperCamelCase__ )
       return tuple(UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]=None ):


       """simple docstring"""







       UpperCamelCase					=  [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      :					Tuple  ,						UpperCamelCase__      :					List[int]  ,						UpperCamelCase__      :					Optional[List[int]] = None ):


       """simple docstring"""







       UpperCamelCase					=  [self.sep_token_id]
       UpperCamelCase					=  [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      :					List[Any]  ,						UpperCamelCase__      :					Union[Dict[str, EncodedInput], BatchEncoding]  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  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:
          UpperCamelCase					=  """attention_mask""" in self.model_input_names

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

          if needs_to_be_padded:
             UpperCamelCase					=  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`
                UpperCamelCase					=  (
                    encoded_inputs["""global_attention_mask"""] + [-1] * difference
                )
             elif self.padding_side == "left":
                UpperCamelCase					=  [-1] * difference + encoded_inputs[
                    """global_attention_mask"""
                ]
             else:
                raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )

       return encoded_inputs





'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   int = 10_00					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   3
  lowerCAmelCase_			:      Dict			=   0
  while a < n:
    if a % 3 == 0 or a % 5 == 0:
      result += a
    elif a % 15 == 0:
      result -= a
    a += 1
  return result


if __name__ == "__main__":
  print(F'''{solution() = }''')



