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


UpperCAmelCase_			    =							logging.get_logger(__name__)


UpperCAmelCase_			    =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_			    =							{
    '''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''',
    },
}

UpperCAmelCase_			    =							{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}




class  __magic_name__  (		__a    ):





   """simple docstring"""




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



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



    """simple docstring"""



    super().__init__(
        _lowercase		,  _lowercase		,  tokenizer_file=_lowercase		,  errors=_lowercase		,  bos_token=_lowercase		,  eos_token=_lowercase		,  sep_token=_lowercase		,  cls_token=_lowercase		,  unk_token=_lowercase		,  pad_token=_lowercase		,  mask_token=_lowercase		,  add_prefix_space=_lowercase		,  trim_offsets=_lowercase		,  **_lowercase		,  )

    _UpperCamelCase:     Optional[int]							= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
    if pre_tok_state.get('''add_prefix_space'''		,  _lowercase							) != add_prefix_space:
     _UpperCamelCase:     List[str]							= getattr(_lowercase		,  pre_tok_state.pop('''type'''							)							)
     _UpperCamelCase:     Dict							= add_prefix_space
     _UpperCamelCase:     List[str]							= pre_tok_class(**_lowercase							)

    _UpperCamelCase:     List[Any]							= add_prefix_space

    # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
    _UpperCamelCase:     Optional[Any]							= '''post_processor'''
    _UpperCamelCase:     List[Any]							= getattr(self.backend_tokenizer		,  _lowercase		,  _lowercase							)
    if tokenizer_component_instance:
     _UpperCamelCase:     int							= 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:     List[str]							= tuple(state['''sep''']							)
     if "cls" in state:
      _UpperCamelCase:     Dict							= tuple(state['''cls''']							)

     _UpperCamelCase:     Optional[int]							= False

     if state.get('''add_prefix_space'''		,  _lowercase							) != add_prefix_space:
      _UpperCamelCase:     Any							= add_prefix_space
      _UpperCamelCase:     Optional[int]							= True

     if state.get('''trim_offsets'''		,  _lowercase							) != trim_offsets:
      _UpperCamelCase:     Dict							= trim_offsets
      _UpperCamelCase:     Union[str, Any]							= True

     if changes_to_apply:
      _UpperCamelCase:     Union[str, Any]							= getattr(_lowercase		,  state.pop('''type'''							)							)
      _UpperCamelCase:     Union[str, Any]							= component_class(**_lowercase							)
      setattr(self.backend_tokenizer		,  _lowercase		,  _lowercase							)



   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def   lowerCAmelCase						(	self					:  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   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= AddedToken(_lowercase		,  lstrip=_lowercase		,  rstrip=_lowercase							) if isinstance(_lowercase		,  _lowercase							) else value
    _UpperCamelCase:     Optional[int]							= value



   def   lowerCAmelCase						(	self					:  List[Any]		,  *_lowercase					:  List[str]		,  **_lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= kwargs.get('''is_split_into_words'''		,  _lowercase							)

    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(*_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  *_lowercase					:  str		,  **_lowercase					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     str							= kwargs.get('''is_split_into_words'''		,  _lowercase							)

    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(*_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  str		,  _lowercase					:  Optional[str] = None							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self._tokenizer.model.save(_lowercase		,  name=_lowercase							)
    return tuple(_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Any		,  _lowercase					:  str=None							):



    """simple docstring"""



    _UpperCamelCase:     Optional[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   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= [self.sep_token_id]
    _UpperCamelCase:     Optional[Any]							= [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   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Union[Dict[str, EncodedInput], BatchEncoding]		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Optional[bool] = None		,  ):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= super()._pad(
        encoded_inputs=_lowercase		,  max_length=_lowercase		,  padding_strategy=_lowercase		,  pad_to_multiple_of=_lowercase		,  return_attention_mask=_lowercase		,  )

    # Load from model defaults
    if return_attention_mask is None:
     _UpperCamelCase:     Any							= '''attention_mask''' in self.model_input_names

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

     if needs_to_be_padded:
      _UpperCamelCase:     Any							= len(_lowercase							) - 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:     Tuple							= (
           encoded_inputs['''global_attention_mask'''] + [-1] * difference
       )
      elif self.padding_side == "left":
       _UpperCamelCase:     Optional[int]							= [-1] * difference + encoded_inputs[
           '''global_attention_mask'''
       ]
      else:
       raise ValueError('''Invalid padding strategy:''' + str(self.padding_side							)							)

    return encoded_inputs
def       lowerCAmelCase_     (		lowercase:							int = 10**9		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= 1
 _UpperCamelCase:     Dict							= 2

 _UpperCamelCase:     Tuple							= 0
 _UpperCamelCase:     int							= 0
 _UpperCamelCase:     Dict							= 0
 while perimeter <= max_perimeter:
  perimeters_sum += perimeter

  prev_value += 2 * value
  value += prev_value

  _UpperCamelCase:     str							= 2 * value + 2 if i % 2 == 0 else 2 * value - 2
  i += 1

 return perimeters_sum


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